Title
string | Abstract
string | Status
string | User
string | text
string | label
int64 | combined_text
string | __index_level_0__
int64 |
|---|---|---|---|---|---|---|---|
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?
|
In this article, we extend the conventional framework of
convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
among abitrary number of time related input maps by constructing a layer of
multiplicative units, which capture the relations among inputs. In many cases,
more than two maps are strongly related, so it is wise to make multiplicative
unit learn relations among more input maps, in other words, to find the optimal
relational-order of each unit. In order to enable our machine to learn
relational order, we developed a reinforcement-learning method whose optimality
is proven to train the network.
|
Disliked
|
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure? : In this article, we extend the conventional framework of
convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
among abitrary number of time related input maps by constructing a layer of
multiplicative units, which capture the relations among inputs. In many cases,
more than two maps are strongly related, so it is wise to make multiplicative
unit learn relations among more input maps, in other words, to find the optimal
relational-order of each unit. In order to enable our machine to learn
relational order, we developed a reinforcement-learning method whose optimality
is proven to train the network.
| 0
|
[email protected] [SEP] Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure? : In this article, we extend the conventional framework of
convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
among abitrary number of time related input maps by constructing a layer of
multiplicative units, which capture the relations among inputs. In many cases,
more than two maps are strongly related, so it is wise to make multiplicative
unit learn relations among more input maps, in other words, to find the optimal
relational-order of each unit. In order to enable our machine to learn
relational order, we developed a reinforcement-learning method whose optimality
is proven to train the network.
| 56
|
|
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey
|
Since the 2004 DARPA Grand Challenge, the autonomous driving technology has
witnessed nearly two decades of rapid development. Particularly, in recent
years, with the application of new sensors and deep learning technologies
extending to the autonomous field, the development of autonomous driving
technology has continued to make breakthroughs. Thus, many carmakers and
high-tech giants dedicated to research and system development of autonomous
driving. However, as the foundation of autonomous driving, the deep learning
technology faces many new security risks. The academic community has proposed
deep learning countermeasures against the adversarial examples and AI backdoor,
and has introduced them into the autonomous driving field for verification.
Deep learning security matters to autonomous driving system security, and then
matters to personal safety, which is an issue that deserves attention and
research.This paper provides an summary of the concepts, developments and
recent research in deep learning security technologies in autonomous driving.
Firstly, we briefly introduce the deep learning framework and pipeline in the
autonomous driving system, which mainly include the deep learning technologies
and algorithms commonly used in this field. Moreover, we focus on the potential
security threats of the deep learning based autonomous driving system in each
functional layer in turn. We reviews the development of deep learning attack
technologies to autonomous driving, investigates the State-of-the-Art
algorithms, and reveals the potential risks. At last, we provides an outlook on
deep learning security in the autonomous driving field and proposes
recommendations for building a safe and trustworthy autonomous driving system.
|
Liked
|
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey : Since the 2004 DARPA Grand Challenge, the autonomous driving technology has
witnessed nearly two decades of rapid development. Particularly, in recent
years, with the application of new sensors and deep learning technologies
extending to the autonomous field, the development of autonomous driving
technology has continued to make breakthroughs. Thus, many carmakers and
high-tech giants dedicated to research and system development of autonomous
driving. However, as the foundation of autonomous driving, the deep learning
technology faces many new security risks. The academic community has proposed
deep learning countermeasures against the adversarial examples and AI backdoor,
and has introduced them into the autonomous driving field for verification.
Deep learning security matters to autonomous driving system security, and then
matters to personal safety, which is an issue that deserves attention and
research.This paper provides an summary of the concepts, developments and
recent research in deep learning security technologies in autonomous driving.
Firstly, we briefly introduce the deep learning framework and pipeline in the
autonomous driving system, which mainly include the deep learning technologies
and algorithms commonly used in this field. Moreover, we focus on the potential
security threats of the deep learning based autonomous driving system in each
functional layer in turn. We reviews the development of deep learning attack
technologies to autonomous driving, investigates the State-of-the-Art
algorithms, and reveals the potential risks. At last, we provides an outlook on
deep learning security in the autonomous driving field and proposes
recommendations for building a safe and trustworthy autonomous driving system.
| 1
|
[email protected] [SEP] Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey : Since the 2004 DARPA Grand Challenge, the autonomous driving technology has
witnessed nearly two decades of rapid development. Particularly, in recent
years, with the application of new sensors and deep learning technologies
extending to the autonomous field, the development of autonomous driving
technology has continued to make breakthroughs. Thus, many carmakers and
high-tech giants dedicated to research and system development of autonomous
driving. However, as the foundation of autonomous driving, the deep learning
technology faces many new security risks. The academic community has proposed
deep learning countermeasures against the adversarial examples and AI backdoor,
and has introduced them into the autonomous driving field for verification.
Deep learning security matters to autonomous driving system security, and then
matters to personal safety, which is an issue that deserves attention and
research.This paper provides an summary of the concepts, developments and
recent research in deep learning security technologies in autonomous driving.
Firstly, we briefly introduce the deep learning framework and pipeline in the
autonomous driving system, which mainly include the deep learning technologies
and algorithms commonly used in this field. Moreover, we focus on the potential
security threats of the deep learning based autonomous driving system in each
functional layer in turn. We reviews the development of deep learning attack
technologies to autonomous driving, investigates the State-of-the-Art
algorithms, and reveals the potential risks. At last, we provides an outlook on
deep learning security in the autonomous driving field and proposes
recommendations for building a safe and trustworthy autonomous driving system.
| 223
|
|
UniDiffGrasp: A Unified Framework Integrating VLM Reasoning and VLM-Guided Part Diffusion for Open-Vocabulary Constrained Grasping with Dual Arms
|
Open-vocabulary, task-oriented grasping of specific functional parts,
particularly with dual arms, remains a key challenge, as current
Vision-Language Models (VLMs), while enhancing task understanding, often
struggle with precise grasp generation within defined constraints and effective
dual-arm coordination. We innovatively propose UniDiffGrasp, a unified
framework integrating VLM reasoning with guided part diffusion to address these
limitations. UniDiffGrasp leverages a VLM to interpret user input and identify
semantic targets (object, part(s), mode), which are then grounded via
open-vocabulary segmentation. Critically, the identified parts directly provide
geometric constraints for a Constrained Grasp Diffusion Field (CGDF) using its
Part-Guided Diffusion, enabling efficient, high-quality 6-DoF grasps without
retraining. For dual-arm tasks, UniDiffGrasp defines distinct target regions,
applies part-guided diffusion per arm, and selects stable cooperative grasps.
Through extensive real-world deployment, UniDiffGrasp achieves grasp success
rates of 0.876 in single-arm and 0.767 in dual-arm scenarios, significantly
surpassing existing state-of-the-art methods, demonstrating its capability to
enable precise and coordinated open-vocabulary grasping in complex real-world
scenarios.
|
Liked
|
UniDiffGrasp: A Unified Framework Integrating VLM Reasoning and VLM-Guided Part Diffusion for Open-Vocabulary Constrained Grasping with Dual Arms : Open-vocabulary, task-oriented grasping of specific functional parts,
particularly with dual arms, remains a key challenge, as current
Vision-Language Models (VLMs), while enhancing task understanding, often
struggle with precise grasp generation within defined constraints and effective
dual-arm coordination. We innovatively propose UniDiffGrasp, a unified
framework integrating VLM reasoning with guided part diffusion to address these
limitations. UniDiffGrasp leverages a VLM to interpret user input and identify
semantic targets (object, part(s), mode), which are then grounded via
open-vocabulary segmentation. Critically, the identified parts directly provide
geometric constraints for a Constrained Grasp Diffusion Field (CGDF) using its
Part-Guided Diffusion, enabling efficient, high-quality 6-DoF grasps without
retraining. For dual-arm tasks, UniDiffGrasp defines distinct target regions,
applies part-guided diffusion per arm, and selects stable cooperative grasps.
Through extensive real-world deployment, UniDiffGrasp achieves grasp success
rates of 0.876 in single-arm and 0.767 in dual-arm scenarios, significantly
surpassing existing state-of-the-art methods, demonstrating its capability to
enable precise and coordinated open-vocabulary grasping in complex real-world
scenarios.
| 1
|
[email protected] [SEP] UniDiffGrasp: A Unified Framework Integrating VLM Reasoning and VLM-Guided Part Diffusion for Open-Vocabulary Constrained Grasping with Dual Arms : Open-vocabulary, task-oriented grasping of specific functional parts,
particularly with dual arms, remains a key challenge, as current
Vision-Language Models (VLMs), while enhancing task understanding, often
struggle with precise grasp generation within defined constraints and effective
dual-arm coordination. We innovatively propose UniDiffGrasp, a unified
framework integrating VLM reasoning with guided part diffusion to address these
limitations. UniDiffGrasp leverages a VLM to interpret user input and identify
semantic targets (object, part(s), mode), which are then grounded via
open-vocabulary segmentation. Critically, the identified parts directly provide
geometric constraints for a Constrained Grasp Diffusion Field (CGDF) using its
Part-Guided Diffusion, enabling efficient, high-quality 6-DoF grasps without
retraining. For dual-arm tasks, UniDiffGrasp defines distinct target regions,
applies part-guided diffusion per arm, and selects stable cooperative grasps.
Through extensive real-world deployment, UniDiffGrasp achieves grasp success
rates of 0.876 in single-arm and 0.767 in dual-arm scenarios, significantly
surpassing existing state-of-the-art methods, demonstrating its capability to
enable precise and coordinated open-vocabulary grasping in complex real-world
scenarios.
| 559
|
|
Design and Development of a Remotely Wire-Driven Walking Robot
|
Operating in environments too harsh or inaccessible for humans is one of the
critical roles expected of robots. However, such environments often pose risks
to electronic components as well. To overcome this, various approaches have
been developed, including autonomous mobile robots without electronics,
hydraulic remotely actuated mobile robots, and long-reach robot arms driven by
wires. Among these, electronics-free autonomous robots cannot make complex
decisions, while hydraulically actuated mobile robots and wire-driven robot
arms are used in harsh environments such as nuclear power plants. Mobile robots
offer greater reach and obstacle avoidance than robot arms, and wire mechanisms
offer broader environmental applicability than hydraulics. However, wire-driven
systems have not been used for remote actuation of mobile robots. In this
study, we propose a novel mechanism called Remote Wire Drive that enables
remote actuation of mobile robots via wires. This mechanism is a series
connection of decoupled joints, a mechanism used in wire-driven robot arms,
adapted for power transmission. We experimentally validated its feasibility by
actuating a wire-driven quadruped robot, which we also developed in this study,
through Remote Wire Drive.
|
Liked
|
Design and Development of a Remotely Wire-Driven Walking Robot : Operating in environments too harsh or inaccessible for humans is one of the
critical roles expected of robots. However, such environments often pose risks
to electronic components as well. To overcome this, various approaches have
been developed, including autonomous mobile robots without electronics,
hydraulic remotely actuated mobile robots, and long-reach robot arms driven by
wires. Among these, electronics-free autonomous robots cannot make complex
decisions, while hydraulically actuated mobile robots and wire-driven robot
arms are used in harsh environments such as nuclear power plants. Mobile robots
offer greater reach and obstacle avoidance than robot arms, and wire mechanisms
offer broader environmental applicability than hydraulics. However, wire-driven
systems have not been used for remote actuation of mobile robots. In this
study, we propose a novel mechanism called Remote Wire Drive that enables
remote actuation of mobile robots via wires. This mechanism is a series
connection of decoupled joints, a mechanism used in wire-driven robot arms,
adapted for power transmission. We experimentally validated its feasibility by
actuating a wire-driven quadruped robot, which we also developed in this study,
through Remote Wire Drive.
| 1
|
[email protected] [SEP] Design and Development of a Remotely Wire-Driven Walking Robot : Operating in environments too harsh or inaccessible for humans is one of the
critical roles expected of robots. However, such environments often pose risks
to electronic components as well. To overcome this, various approaches have
been developed, including autonomous mobile robots without electronics,
hydraulic remotely actuated mobile robots, and long-reach robot arms driven by
wires. Among these, electronics-free autonomous robots cannot make complex
decisions, while hydraulically actuated mobile robots and wire-driven robot
arms are used in harsh environments such as nuclear power plants. Mobile robots
offer greater reach and obstacle avoidance than robot arms, and wire mechanisms
offer broader environmental applicability than hydraulics. However, wire-driven
systems have not been used for remote actuation of mobile robots. In this
study, we propose a novel mechanism called Remote Wire Drive that enables
remote actuation of mobile robots via wires. This mechanism is a series
connection of decoupled joints, a mechanism used in wire-driven robot arms,
adapted for power transmission. We experimentally validated its feasibility by
actuating a wire-driven quadruped robot, which we also developed in this study,
through Remote Wire Drive.
| 19
|
|
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice
|
Recently, deep models have had considerable success in several tasks,
especially with low-level representations. However, effective learning from
sparse noisy samples is a major challenge in most deep models, especially in
domains with structured representations. Inspired by the proven success of
human guided machine learning, we propose Knowledge-augmented Column Networks,
a relational deep learning framework that leverages human advice/knowledge to
learn better models in presence of sparsity and systematic noise.
|
Disliked
|
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice : Recently, deep models have had considerable success in several tasks,
especially with low-level representations. However, effective learning from
sparse noisy samples is a major challenge in most deep models, especially in
domains with structured representations. Inspired by the proven success of
human guided machine learning, we propose Knowledge-augmented Column Networks,
a relational deep learning framework that leverages human advice/knowledge to
learn better models in presence of sparsity and systematic noise.
| 0
|
[email protected] [SEP] Knowledge-augmented Column Networks: Guiding Deep Learning with Advice : Recently, deep models have had considerable success in several tasks,
especially with low-level representations. However, effective learning from
sparse noisy samples is a major challenge in most deep models, especially in
domains with structured representations. Inspired by the proven success of
human guided machine learning, we propose Knowledge-augmented Column Networks,
a relational deep learning framework that leverages human advice/knowledge to
learn better models in presence of sparsity and systematic noise.
| 255
|
|
Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge
|
Medical vision-and-language pre-training (Med-VLP) has received considerable
attention owing to its applicability to extracting generic vision-and-language
representations from medical images and texts. Most existing methods mainly
contain three elements: uni-modal encoders (i.e., a vision encoder and a
language encoder), a multi-modal fusion module, and pretext tasks, with few
studies considering the importance of medical domain expert knowledge and
explicitly exploiting such knowledge to facilitate Med-VLP. Although there
exist knowledge-enhanced vision-and-language pre-training (VLP) methods in the
general domain, most require off-the-shelf toolkits (e.g., object detectors and
scene graph parsers), which are unavailable in the medical domain. In this
paper, we propose a systematic and effective approach to enhance Med-VLP by
structured medical knowledge from three perspectives. First, considering
knowledge can be regarded as the intermediate medium between vision and
language, we align the representations of the vision encoder and the language
encoder through knowledge. Second, we inject knowledge into the multi-modal
fusion model to enable the model to perform reasoning using knowledge as the
supplementation of the input image and text. Third, we guide the model to put
emphasis on the most critical information in images and texts by designing
knowledge-induced pretext tasks. To perform a comprehensive evaluation and
facilitate further research, we construct a medical vision-and-language
benchmark including three tasks. Experimental results illustrate the
effectiveness of our approach, where state-of-the-art performance is achieved
on all downstream tasks. Further analyses explore the effects of different
components of our approach and various settings of pre-training.
|
Liked
|
Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge : Medical vision-and-language pre-training (Med-VLP) has received considerable
attention owing to its applicability to extracting generic vision-and-language
representations from medical images and texts. Most existing methods mainly
contain three elements: uni-modal encoders (i.e., a vision encoder and a
language encoder), a multi-modal fusion module, and pretext tasks, with few
studies considering the importance of medical domain expert knowledge and
explicitly exploiting such knowledge to facilitate Med-VLP. Although there
exist knowledge-enhanced vision-and-language pre-training (VLP) methods in the
general domain, most require off-the-shelf toolkits (e.g., object detectors and
scene graph parsers), which are unavailable in the medical domain. In this
paper, we propose a systematic and effective approach to enhance Med-VLP by
structured medical knowledge from three perspectives. First, considering
knowledge can be regarded as the intermediate medium between vision and
language, we align the representations of the vision encoder and the language
encoder through knowledge. Second, we inject knowledge into the multi-modal
fusion model to enable the model to perform reasoning using knowledge as the
supplementation of the input image and text. Third, we guide the model to put
emphasis on the most critical information in images and texts by designing
knowledge-induced pretext tasks. To perform a comprehensive evaluation and
facilitate further research, we construct a medical vision-and-language
benchmark including three tasks. Experimental results illustrate the
effectiveness of our approach, where state-of-the-art performance is achieved
on all downstream tasks. Further analyses explore the effects of different
components of our approach and various settings of pre-training.
| 1
|
[email protected] [SEP] Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge : Medical vision-and-language pre-training (Med-VLP) has received considerable
attention owing to its applicability to extracting generic vision-and-language
representations from medical images and texts. Most existing methods mainly
contain three elements: uni-modal encoders (i.e., a vision encoder and a
language encoder), a multi-modal fusion module, and pretext tasks, with few
studies considering the importance of medical domain expert knowledge and
explicitly exploiting such knowledge to facilitate Med-VLP. Although there
exist knowledge-enhanced vision-and-language pre-training (VLP) methods in the
general domain, most require off-the-shelf toolkits (e.g., object detectors and
scene graph parsers), which are unavailable in the medical domain. In this
paper, we propose a systematic and effective approach to enhance Med-VLP by
structured medical knowledge from three perspectives. First, considering
knowledge can be regarded as the intermediate medium between vision and
language, we align the representations of the vision encoder and the language
encoder through knowledge. Second, we inject knowledge into the multi-modal
fusion model to enable the model to perform reasoning using knowledge as the
supplementation of the input image and text. Third, we guide the model to put
emphasis on the most critical information in images and texts by designing
knowledge-induced pretext tasks. To perform a comprehensive evaluation and
facilitate further research, we construct a medical vision-and-language
benchmark including three tasks. Experimental results illustrate the
effectiveness of our approach, where state-of-the-art performance is achieved
on all downstream tasks. Further analyses explore the effects of different
components of our approach and various settings of pre-training.
| 349
|
|
Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement
|
We investigate the problem of coordinating two robot arms to solve
non-monotone tabletop multi-object rearrangement tasks. In a non-monotone
rearrangement task, complex object-object dependencies exist that require
moving some objects multiple times to solve an instance. In working with two
arms in a large workspace, some objects must be handed off between the robots,
which further complicates the planning process. For the challenging dual-arm
tabletop rearrangement problem, we develop effective task planning algorithms
for scheduling the pick-n-place sequence that can be properly distributed
between the two arms. We show that, even without using a sophisticated motion
planner, our method achieves significant time savings in comparison to greedy
approaches and naive parallelization of single-robot plans.
|
Liked
|
Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement : We investigate the problem of coordinating two robot arms to solve
non-monotone tabletop multi-object rearrangement tasks. In a non-monotone
rearrangement task, complex object-object dependencies exist that require
moving some objects multiple times to solve an instance. In working with two
arms in a large workspace, some objects must be handed off between the robots,
which further complicates the planning process. For the challenging dual-arm
tabletop rearrangement problem, we develop effective task planning algorithms
for scheduling the pick-n-place sequence that can be properly distributed
between the two arms. We show that, even without using a sophisticated motion
planner, our method achieves significant time savings in comparison to greedy
approaches and naive parallelization of single-robot plans.
| 1
|
[email protected] [SEP] Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement : We investigate the problem of coordinating two robot arms to solve
non-monotone tabletop multi-object rearrangement tasks. In a non-monotone
rearrangement task, complex object-object dependencies exist that require
moving some objects multiple times to solve an instance. In working with two
arms in a large workspace, some objects must be handed off between the robots,
which further complicates the planning process. For the challenging dual-arm
tabletop rearrangement problem, we develop effective task planning algorithms
for scheduling the pick-n-place sequence that can be properly distributed
between the two arms. We show that, even without using a sophisticated motion
planner, our method achieves significant time savings in comparison to greedy
approaches and naive parallelization of single-robot plans.
| 21
|
|
Deep Learning of Representations: Looking Forward
|
Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges.
|
Liked
|
Deep Learning of Representations: Looking Forward : Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges.
| 1
|
[email protected] [SEP] Deep Learning of Representations: Looking Forward : Deep learning research aims at discovering learning algorithms that discover
multiple levels of distributed representations, with higher levels representing
more abstract concepts. Although the study of deep learning has already led to
impressive theoretical results, learning algorithms and breakthrough
experiments, several challenges lie ahead. This paper proposes to examine some
of these challenges, centering on the questions of scaling deep learning
algorithms to much larger models and datasets, reducing optimization
difficulties due to ill-conditioning or local minima, designing more efficient
and powerful inference and sampling procedures, and learning to disentangle the
factors of variation underlying the observed data. It also proposes a few
forward-looking research directions aimed at overcoming these challenges.
| 252
|
|
Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand
|
This paper investigates the impact of cooperative perception on autonomous
driving decision making on urban roads. The extended perception range
contributed by the cooperative perception can be properly leveraged to address
the implicit dependencies within the vehicles, thereby the vehicle decision
making performance can be improved. Meanwhile, we acknowledge the inherent
limitation of wireless communication and propose a Cooperative Perception on
Demand (CPoD) strategy, where the cooperative perception will only be activated
when the extended perception range is necessary for proper situation-awareness.
The situation-aware decision making with CPoD is modeled as a Partially
Observable Markov Decision Process (POMDP) and solved in an online manner. The
evaluation results demonstrate that the proposed approach can function safely
and efficiently for autonomous driving on urban roads.
|
Disliked
|
Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand : This paper investigates the impact of cooperative perception on autonomous
driving decision making on urban roads. The extended perception range
contributed by the cooperative perception can be properly leveraged to address
the implicit dependencies within the vehicles, thereby the vehicle decision
making performance can be improved. Meanwhile, we acknowledge the inherent
limitation of wireless communication and propose a Cooperative Perception on
Demand (CPoD) strategy, where the cooperative perception will only be activated
when the extended perception range is necessary for proper situation-awareness.
The situation-aware decision making with CPoD is modeled as a Partially
Observable Markov Decision Process (POMDP) and solved in an online manner. The
evaluation results demonstrate that the proposed approach can function safely
and efficiently for autonomous driving on urban roads.
| 0
|
[email protected] [SEP] Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand : This paper investigates the impact of cooperative perception on autonomous
driving decision making on urban roads. The extended perception range
contributed by the cooperative perception can be properly leveraged to address
the implicit dependencies within the vehicles, thereby the vehicle decision
making performance can be improved. Meanwhile, we acknowledge the inherent
limitation of wireless communication and propose a Cooperative Perception on
Demand (CPoD) strategy, where the cooperative perception will only be activated
when the extended perception range is necessary for proper situation-awareness.
The situation-aware decision making with CPoD is modeled as a Partially
Observable Markov Decision Process (POMDP) and solved in an online manner. The
evaluation results demonstrate that the proposed approach can function safely
and efficiently for autonomous driving on urban roads.
| 274
|
|
PSL is Dead. Long Live PSL
|
Property Specification Language (PSL) is a form of temporal logic that has
been mainly used in discrete domains (e.g. formal hardware verification). In
this paper, we show that by merging machine learning techniques with PSL
monitors, we can extend PSL to work on continuous domains. We apply this
technique in machine learning-based anomaly detection to analyze scenarios of
real-time streaming events from continuous variables in order to detect
abnormal behaviors of a system. By using machine learning with formal models,
we leverage the strengths of both machine learning methods and formal semantics
of time. On one hand, machine learning techniques can produce distributions on
continuous variables, where abnormalities can be captured as deviations from
the distributions. On the other hand, formal methods can characterize discrete
temporal behaviors and relations that cannot be easily learned by machine
learning techniques. Interestingly, the anomalies detected by machine learning
and the underlying time representation used are discrete events. We implemented
a temporal monitoring package (TEF) that operates in conjunction with normal
data science packages for anomaly detection machine learning systems, and we
show that TEF can be used to perform accurate interpretation of temporal
correlation between events.
|
Disliked
|
PSL is Dead. Long Live PSL : Property Specification Language (PSL) is a form of temporal logic that has
been mainly used in discrete domains (e.g. formal hardware verification). In
this paper, we show that by merging machine learning techniques with PSL
monitors, we can extend PSL to work on continuous domains. We apply this
technique in machine learning-based anomaly detection to analyze scenarios of
real-time streaming events from continuous variables in order to detect
abnormal behaviors of a system. By using machine learning with formal models,
we leverage the strengths of both machine learning methods and formal semantics
of time. On one hand, machine learning techniques can produce distributions on
continuous variables, where abnormalities can be captured as deviations from
the distributions. On the other hand, formal methods can characterize discrete
temporal behaviors and relations that cannot be easily learned by machine
learning techniques. Interestingly, the anomalies detected by machine learning
and the underlying time representation used are discrete events. We implemented
a temporal monitoring package (TEF) that operates in conjunction with normal
data science packages for anomaly detection machine learning systems, and we
show that TEF can be used to perform accurate interpretation of temporal
correlation between events.
| 0
|
[email protected] [SEP] PSL is Dead. Long Live PSL : Property Specification Language (PSL) is a form of temporal logic that has
been mainly used in discrete domains (e.g. formal hardware verification). In
this paper, we show that by merging machine learning techniques with PSL
monitors, we can extend PSL to work on continuous domains. We apply this
technique in machine learning-based anomaly detection to analyze scenarios of
real-time streaming events from continuous variables in order to detect
abnormal behaviors of a system. By using machine learning with formal models,
we leverage the strengths of both machine learning methods and formal semantics
of time. On one hand, machine learning techniques can produce distributions on
continuous variables, where abnormalities can be captured as deviations from
the distributions. On the other hand, formal methods can characterize discrete
temporal behaviors and relations that cannot be easily learned by machine
learning techniques. Interestingly, the anomalies detected by machine learning
and the underlying time representation used are discrete events. We implemented
a temporal monitoring package (TEF) that operates in conjunction with normal
data science packages for anomaly detection machine learning systems, and we
show that TEF can be used to perform accurate interpretation of temporal
correlation between events.
| 147
|
|
Joint Training of Deep Boltzmann Machines
|
We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks.
|
Disliked
|
Joint Training of Deep Boltzmann Machines : We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks.
| 0
|
[email protected] [SEP] Joint Training of Deep Boltzmann Machines : We introduce a new method for training deep Boltzmann machines jointly. Prior
methods require an initial learning pass that trains the deep Boltzmann machine
greedily, one layer at a time, or do not perform well on classifi- cation
tasks.
| 41
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Optimal Scheduling of a Dual-Arm Robot for Efficient Strawberry Harvesting in Plant Factories
|
Plant factory cultivation is widely recognized for its ability to optimize
resource use and boost crop yields. To further increase the efficiency in these
environments, we propose a mixed-integer linear programming (MILP) framework
that systematically schedules and coordinates dual-arm harvesting tasks,
minimizing the overall harvesting makespan based on pre-mapped fruit locations.
Specifically, we focus on a specialized dual-arm harvesting robot and employ
pose coverage analysis of its end effector to maximize picking reachability.
Additionally, we compare the performance of the dual-arm configuration with
that of a single-arm vehicle, demonstrating that the dual-arm system can nearly
double efficiency when fruit densities are roughly equal on both sides.
Extensive simulations show a 10-20% increase in throughput and a significant
reduction in the number of stops compared to non-optimized methods. These
results underscore the advantages of an optimal scheduling approach in
improving the scalability and efficiency of robotic harvesting in plant
factories.
|
Liked
|
Optimal Scheduling of a Dual-Arm Robot for Efficient Strawberry Harvesting in Plant Factories : Plant factory cultivation is widely recognized for its ability to optimize
resource use and boost crop yields. To further increase the efficiency in these
environments, we propose a mixed-integer linear programming (MILP) framework
that systematically schedules and coordinates dual-arm harvesting tasks,
minimizing the overall harvesting makespan based on pre-mapped fruit locations.
Specifically, we focus on a specialized dual-arm harvesting robot and employ
pose coverage analysis of its end effector to maximize picking reachability.
Additionally, we compare the performance of the dual-arm configuration with
that of a single-arm vehicle, demonstrating that the dual-arm system can nearly
double efficiency when fruit densities are roughly equal on both sides.
Extensive simulations show a 10-20% increase in throughput and a significant
reduction in the number of stops compared to non-optimized methods. These
results underscore the advantages of an optimal scheduling approach in
improving the scalability and efficiency of robotic harvesting in plant
factories.
| 1
|
[email protected] [SEP] Optimal Scheduling of a Dual-Arm Robot for Efficient Strawberry Harvesting in Plant Factories : Plant factory cultivation is widely recognized for its ability to optimize
resource use and boost crop yields. To further increase the efficiency in these
environments, we propose a mixed-integer linear programming (MILP) framework
that systematically schedules and coordinates dual-arm harvesting tasks,
minimizing the overall harvesting makespan based on pre-mapped fruit locations.
Specifically, we focus on a specialized dual-arm harvesting robot and employ
pose coverage analysis of its end effector to maximize picking reachability.
Additionally, we compare the performance of the dual-arm configuration with
that of a single-arm vehicle, demonstrating that the dual-arm system can nearly
double efficiency when fruit densities are roughly equal on both sides.
Extensive simulations show a 10-20% increase in throughput and a significant
reduction in the number of stops compared to non-optimized methods. These
results underscore the advantages of an optimal scheduling approach in
improving the scalability and efficiency of robotic harvesting in plant
factories.
| 476
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|
Inspiring Computer Vision System Solutions
|
The "digital Michelangelo project" was a seminal computer vision project in
the early 2000's that pushed the capabilities of acquisition systems and
involved multiple people from diverse fields, many of whom are now leaders in
industry and academia. Reviewing this project with modern eyes provides us with
the opportunity to reflect on several issues, relevant now as then to the field
of computer vision and research in general, that go beyond the technical
aspects of the work.
This article was written in the context of a reading group competition at the
week-long International Computer Vision Summer School 2017 (ICVSS) on Sicily,
Italy. To deepen the participants understanding of computer vision and to
foster a sense of community, various reading groups were tasked to highlight
important lessons which may be learned from provided literature, going beyond
the contents of the paper. This report is the winning entry of this guided
discourse (Fig. 1). The authors closely examined the origins, fruits and most
importantly lessons about research in general which may be distilled from the
"digital Michelangelo project". Discussions leading to this report were held
within the group as well as with Hao Li, the group mentor.
|
Disliked
|
Inspiring Computer Vision System Solutions : The "digital Michelangelo project" was a seminal computer vision project in
the early 2000's that pushed the capabilities of acquisition systems and
involved multiple people from diverse fields, many of whom are now leaders in
industry and academia. Reviewing this project with modern eyes provides us with
the opportunity to reflect on several issues, relevant now as then to the field
of computer vision and research in general, that go beyond the technical
aspects of the work.
This article was written in the context of a reading group competition at the
week-long International Computer Vision Summer School 2017 (ICVSS) on Sicily,
Italy. To deepen the participants understanding of computer vision and to
foster a sense of community, various reading groups were tasked to highlight
important lessons which may be learned from provided literature, going beyond
the contents of the paper. This report is the winning entry of this guided
discourse (Fig. 1). The authors closely examined the origins, fruits and most
importantly lessons about research in general which may be distilled from the
"digital Michelangelo project". Discussions leading to this report were held
within the group as well as with Hao Li, the group mentor.
| 0
|
[email protected] [SEP] Inspiring Computer Vision System Solutions : The "digital Michelangelo project" was a seminal computer vision project in
the early 2000's that pushed the capabilities of acquisition systems and
involved multiple people from diverse fields, many of whom are now leaders in
industry and academia. Reviewing this project with modern eyes provides us with
the opportunity to reflect on several issues, relevant now as then to the field
of computer vision and research in general, that go beyond the technical
aspects of the work.
This article was written in the context of a reading group competition at the
week-long International Computer Vision Summer School 2017 (ICVSS) on Sicily,
Italy. To deepen the participants understanding of computer vision and to
foster a sense of community, various reading groups were tasked to highlight
important lessons which may be learned from provided literature, going beyond
the contents of the paper. This report is the winning entry of this guided
discourse (Fig. 1). The authors closely examined the origins, fruits and most
importantly lessons about research in general which may be distilled from the
"digital Michelangelo project". Discussions leading to this report were held
within the group as well as with Hao Li, the group mentor.
| 355
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|
Modeling Generalization in Machine Learning: A Methodological and Computational Study
|
As machine learning becomes more and more available to the general public,
theoretical questions are turning into pressing practical issues. Possibly, one
of the most relevant concerns is the assessment of our confidence in trusting
machine learning predictions. In many real-world cases, it is of utmost
importance to estimate the capabilities of a machine learning algorithm to
generalize, i.e., to provide accurate predictions on unseen data, depending on
the characteristics of the target problem. In this work, we perform a
meta-analysis of 109 publicly-available classification data sets, modeling
machine learning generalization as a function of a variety of data set
characteristics, ranging from number of samples to intrinsic dimensionality,
from class-wise feature skewness to $F1$ evaluated on test samples falling
outside the convex hull of the training set. Experimental results demonstrate
the relevance of using the concept of the convex hull of the training data in
assessing machine learning generalization, by emphasizing the difference
between interpolated and extrapolated predictions. Besides several predictable
correlations, we observe unexpectedly weak associations between the
generalization ability of machine learning models and all metrics related to
dimensionality, thus challenging the common assumption that the \textit{curse
of dimensionality} might impair generalization in machine learning.
|
Disliked
|
Modeling Generalization in Machine Learning: A Methodological and Computational Study : As machine learning becomes more and more available to the general public,
theoretical questions are turning into pressing practical issues. Possibly, one
of the most relevant concerns is the assessment of our confidence in trusting
machine learning predictions. In many real-world cases, it is of utmost
importance to estimate the capabilities of a machine learning algorithm to
generalize, i.e., to provide accurate predictions on unseen data, depending on
the characteristics of the target problem. In this work, we perform a
meta-analysis of 109 publicly-available classification data sets, modeling
machine learning generalization as a function of a variety of data set
characteristics, ranging from number of samples to intrinsic dimensionality,
from class-wise feature skewness to $F1$ evaluated on test samples falling
outside the convex hull of the training set. Experimental results demonstrate
the relevance of using the concept of the convex hull of the training data in
assessing machine learning generalization, by emphasizing the difference
between interpolated and extrapolated predictions. Besides several predictable
correlations, we observe unexpectedly weak associations between the
generalization ability of machine learning models and all metrics related to
dimensionality, thus challenging the common assumption that the \textit{curse
of dimensionality} might impair generalization in machine learning.
| 0
|
[email protected] [SEP] Modeling Generalization in Machine Learning: A Methodological and Computational Study : As machine learning becomes more and more available to the general public,
theoretical questions are turning into pressing practical issues. Possibly, one
of the most relevant concerns is the assessment of our confidence in trusting
machine learning predictions. In many real-world cases, it is of utmost
importance to estimate the capabilities of a machine learning algorithm to
generalize, i.e., to provide accurate predictions on unseen data, depending on
the characteristics of the target problem. In this work, we perform a
meta-analysis of 109 publicly-available classification data sets, modeling
machine learning generalization as a function of a variety of data set
characteristics, ranging from number of samples to intrinsic dimensionality,
from class-wise feature skewness to $F1$ evaluated on test samples falling
outside the convex hull of the training set. Experimental results demonstrate
the relevance of using the concept of the convex hull of the training data in
assessing machine learning generalization, by emphasizing the difference
between interpolated and extrapolated predictions. Besides several predictable
correlations, we observe unexpectedly weak associations between the
generalization ability of machine learning models and all metrics related to
dimensionality, thus challenging the common assumption that the \textit{curse
of dimensionality} might impair generalization in machine learning.
| 117
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|
Development of a Tendon Driven Variable Stiffness Continuum Robot with Layer Jamming
|
The purpose of this research is to design, fabricate and test a tendon driven
a continuum soft robot with three modular segments, each of which has a tunable
stiffness enabled by layer jamming technology. Compared with previous studies,
the robotic arm design of this project has a modular structure, which means the
length of the robotic arm can be adjusted by addition of extra arm
modules/segments to the existing robotic prototype. Furthermore, the new arm
prototype supports motion within a 3-dimensional space. To achieve the goals,
the design and fabrication for the variable stiffness robotic arm with
compliant main structure and layer jamming mechanism has already been finished.
Design and fabrication of the connector has also been finished to integrate
several link modules into one robotic arm with multiple segments. The actuator
located at the base of the arm has already been designed and tested. Finally, a
stiffness test of one arm segment was conducted to verifying the load carrying
capacity of the variable stiffness robotic arm, then the stiffness ratio of the
layer jammed structure was calculated to analyze the stiffness improvement
compared with unstiffened soft robot.
|
Liked
|
Development of a Tendon Driven Variable Stiffness Continuum Robot with Layer Jamming : The purpose of this research is to design, fabricate and test a tendon driven
a continuum soft robot with three modular segments, each of which has a tunable
stiffness enabled by layer jamming technology. Compared with previous studies,
the robotic arm design of this project has a modular structure, which means the
length of the robotic arm can be adjusted by addition of extra arm
modules/segments to the existing robotic prototype. Furthermore, the new arm
prototype supports motion within a 3-dimensional space. To achieve the goals,
the design and fabrication for the variable stiffness robotic arm with
compliant main structure and layer jamming mechanism has already been finished.
Design and fabrication of the connector has also been finished to integrate
several link modules into one robotic arm with multiple segments. The actuator
located at the base of the arm has already been designed and tested. Finally, a
stiffness test of one arm segment was conducted to verifying the load carrying
capacity of the variable stiffness robotic arm, then the stiffness ratio of the
layer jammed structure was calculated to analyze the stiffness improvement
compared with unstiffened soft robot.
| 1
|
[email protected] [SEP] Development of a Tendon Driven Variable Stiffness Continuum Robot with Layer Jamming : The purpose of this research is to design, fabricate and test a tendon driven
a continuum soft robot with three modular segments, each of which has a tunable
stiffness enabled by layer jamming technology. Compared with previous studies,
the robotic arm design of this project has a modular structure, which means the
length of the robotic arm can be adjusted by addition of extra arm
modules/segments to the existing robotic prototype. Furthermore, the new arm
prototype supports motion within a 3-dimensional space. To achieve the goals,
the design and fabrication for the variable stiffness robotic arm with
compliant main structure and layer jamming mechanism has already been finished.
Design and fabrication of the connector has also been finished to integrate
several link modules into one robotic arm with multiple segments. The actuator
located at the base of the arm has already been designed and tested. Finally, a
stiffness test of one arm segment was conducted to verifying the load carrying
capacity of the variable stiffness robotic arm, then the stiffness ratio of the
layer jammed structure was calculated to analyze the stiffness improvement
compared with unstiffened soft robot.
| 389
|
|
Lecture Notes: Optimization for Machine Learning
|
Lecture notes on optimization for machine learning, derived from a course at
Princeton University and tutorials given in MLSS, Buenos Aires, as well as
Simons Foundation, Berkeley.
|
Liked
|
Lecture Notes: Optimization for Machine Learning : Lecture notes on optimization for machine learning, derived from a course at
Princeton University and tutorials given in MLSS, Buenos Aires, as well as
Simons Foundation, Berkeley.
| 1
|
[email protected] [SEP] Lecture Notes: Optimization for Machine Learning : Lecture notes on optimization for machine learning, derived from a course at
Princeton University and tutorials given in MLSS, Buenos Aires, as well as
Simons Foundation, Berkeley.
| 0
|
|
Extremal Configuration of Robot Arms in Three Dimensions
|
We define a volume function for a robot arms in 3-dimensional Euclidean space
and give geometric conditions for its critical points. For 3-arms this volume
function is an exact topological Morse function on the 3-sphere.
|
Liked
|
Extremal Configuration of Robot Arms in Three Dimensions : We define a volume function for a robot arms in 3-dimensional Euclidean space
and give geometric conditions for its critical points. For 3-arms this volume
function is an exact topological Morse function on the 3-sphere.
| 1
|
[email protected] [SEP] Extremal Configuration of Robot Arms in Three Dimensions : We define a volume function for a robot arms in 3-dimensional Euclidean space
and give geometric conditions for its critical points. For 3-arms this volume
function is an exact topological Morse function on the 3-sphere.
| 546
|
|
Fairness in Deep Learning: A Computational Perspective
|
Deep learning is increasingly being used in high-stake decision making
applications that affect individual lives. However, deep learning models might
exhibit algorithmic discrimination behaviors with respect to protected groups,
potentially posing negative impacts on individuals and society. Therefore,
fairness in deep learning has attracted tremendous attention recently. We
provide a review covering recent progresses to tackle algorithmic fairness
problems of deep learning from the computational perspective. Specifically, we
show that interpretability can serve as a useful ingredient to diagnose the
reasons that lead to algorithmic discrimination. We also discuss fairness
mitigation approaches categorized according to three stages of deep learning
life-cycle, aiming to push forward the area of fairness in deep learning and
build genuinely fair and reliable deep learning systems.
|
Liked
|
Fairness in Deep Learning: A Computational Perspective : Deep learning is increasingly being used in high-stake decision making
applications that affect individual lives. However, deep learning models might
exhibit algorithmic discrimination behaviors with respect to protected groups,
potentially posing negative impacts on individuals and society. Therefore,
fairness in deep learning has attracted tremendous attention recently. We
provide a review covering recent progresses to tackle algorithmic fairness
problems of deep learning from the computational perspective. Specifically, we
show that interpretability can serve as a useful ingredient to diagnose the
reasons that lead to algorithmic discrimination. We also discuss fairness
mitigation approaches categorized according to three stages of deep learning
life-cycle, aiming to push forward the area of fairness in deep learning and
build genuinely fair and reliable deep learning systems.
| 1
|
[email protected] [SEP] Fairness in Deep Learning: A Computational Perspective : Deep learning is increasingly being used in high-stake decision making
applications that affect individual lives. However, deep learning models might
exhibit algorithmic discrimination behaviors with respect to protected groups,
potentially posing negative impacts on individuals and society. Therefore,
fairness in deep learning has attracted tremendous attention recently. We
provide a review covering recent progresses to tackle algorithmic fairness
problems of deep learning from the computational perspective. Specifically, we
show that interpretability can serve as a useful ingredient to diagnose the
reasons that lead to algorithmic discrimination. We also discuss fairness
mitigation approaches categorized according to three stages of deep learning
life-cycle, aiming to push forward the area of fairness in deep learning and
build genuinely fair and reliable deep learning systems.
| 224
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|
Intelligent Perception System for Vehicle-Road Cooperation
|
With the development of autonomous driving, the improvement of autonomous
driving technology for individual vehicles has reached the bottleneck. The
advancement of vehicle-road cooperation autonomous driving technology can
expand the vehicle's perception range, supplement the perception blind area and
improve the perception accuracy, to promote the development of autonomous
driving technology and achieve vehicle-road integration. This project mainly
uses lidar to develop data fusion schemes to realize the sharing and
combination of vehicle and road equipment data and achieve the detection and
tracking of dynamic targets. At the same time, some test scenarios for the
vehicle-road cooperative system were designed and used to test our vehicle-road
cooperative awareness system, which proved the advantages of vehicle-road
cooperative autonomous driving over single-vehicle autonomous driving.
|
Liked
|
Intelligent Perception System for Vehicle-Road Cooperation : With the development of autonomous driving, the improvement of autonomous
driving technology for individual vehicles has reached the bottleneck. The
advancement of vehicle-road cooperation autonomous driving technology can
expand the vehicle's perception range, supplement the perception blind area and
improve the perception accuracy, to promote the development of autonomous
driving technology and achieve vehicle-road integration. This project mainly
uses lidar to develop data fusion schemes to realize the sharing and
combination of vehicle and road equipment data and achieve the detection and
tracking of dynamic targets. At the same time, some test scenarios for the
vehicle-road cooperative system were designed and used to test our vehicle-road
cooperative awareness system, which proved the advantages of vehicle-road
cooperative autonomous driving over single-vehicle autonomous driving.
| 1
|
[email protected] [SEP] Intelligent Perception System for Vehicle-Road Cooperation : With the development of autonomous driving, the improvement of autonomous
driving technology for individual vehicles has reached the bottleneck. The
advancement of vehicle-road cooperation autonomous driving technology can
expand the vehicle's perception range, supplement the perception blind area and
improve the perception accuracy, to promote the development of autonomous
driving technology and achieve vehicle-road integration. This project mainly
uses lidar to develop data fusion schemes to realize the sharing and
combination of vehicle and road equipment data and achieve the detection and
tracking of dynamic targets. At the same time, some test scenarios for the
vehicle-road cooperative system were designed and used to test our vehicle-road
cooperative awareness system, which proved the advantages of vehicle-road
cooperative autonomous driving over single-vehicle autonomous driving.
| 288
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|
Efficient Deep Feature Learning and Extraction via StochasticNets
|
Deep neural networks are a powerful tool for feature learning and extraction
given their ability to model high-level abstractions in highly complex data.
One area worth exploring in feature learning and extraction using deep neural
networks is efficient neural connectivity formation for faster feature learning
and extraction. Motivated by findings of stochastic synaptic connectivity
formation in the brain as well as the brain's uncanny ability to efficiently
represent information, we propose the efficient learning and extraction of
features via StochasticNets, where sparsely-connected deep neural networks can
be formed via stochastic connectivity between neurons. To evaluate the
feasibility of such a deep neural network architecture for feature learning and
extraction, we train deep convolutional StochasticNets to learn abstract
features using the CIFAR-10 dataset, and extract the learned features from
images to perform classification on the SVHN and STL-10 datasets. Experimental
results show that features learned using deep convolutional StochasticNets,
with fewer neural connections than conventional deep convolutional neural
networks, can allow for better or comparable classification accuracy than
conventional deep neural networks: relative test error decrease of ~4.5% for
classification on the STL-10 dataset and ~1% for classification on the SVHN
dataset. Furthermore, it was shown that the deep features extracted using deep
convolutional StochasticNets can provide comparable classification accuracy
even when only 10% of the training data is used for feature learning. Finally,
it was also shown that significant gains in feature extraction speed can be
achieved in embedded applications using StochasticNets. As such, StochasticNets
allow for faster feature learning and extraction performance while facilitate
for better or comparable accuracy performances.
|
Liked
|
Efficient Deep Feature Learning and Extraction via StochasticNets : Deep neural networks are a powerful tool for feature learning and extraction
given their ability to model high-level abstractions in highly complex data.
One area worth exploring in feature learning and extraction using deep neural
networks is efficient neural connectivity formation for faster feature learning
and extraction. Motivated by findings of stochastic synaptic connectivity
formation in the brain as well as the brain's uncanny ability to efficiently
represent information, we propose the efficient learning and extraction of
features via StochasticNets, where sparsely-connected deep neural networks can
be formed via stochastic connectivity between neurons. To evaluate the
feasibility of such a deep neural network architecture for feature learning and
extraction, we train deep convolutional StochasticNets to learn abstract
features using the CIFAR-10 dataset, and extract the learned features from
images to perform classification on the SVHN and STL-10 datasets. Experimental
results show that features learned using deep convolutional StochasticNets,
with fewer neural connections than conventional deep convolutional neural
networks, can allow for better or comparable classification accuracy than
conventional deep neural networks: relative test error decrease of ~4.5% for
classification on the STL-10 dataset and ~1% for classification on the SVHN
dataset. Furthermore, it was shown that the deep features extracted using deep
convolutional StochasticNets can provide comparable classification accuracy
even when only 10% of the training data is used for feature learning. Finally,
it was also shown that significant gains in feature extraction speed can be
achieved in embedded applications using StochasticNets. As such, StochasticNets
allow for faster feature learning and extraction performance while facilitate
for better or comparable accuracy performances.
| 1
|
[email protected] [SEP] Efficient Deep Feature Learning and Extraction via StochasticNets : Deep neural networks are a powerful tool for feature learning and extraction
given their ability to model high-level abstractions in highly complex data.
One area worth exploring in feature learning and extraction using deep neural
networks is efficient neural connectivity formation for faster feature learning
and extraction. Motivated by findings of stochastic synaptic connectivity
formation in the brain as well as the brain's uncanny ability to efficiently
represent information, we propose the efficient learning and extraction of
features via StochasticNets, where sparsely-connected deep neural networks can
be formed via stochastic connectivity between neurons. To evaluate the
feasibility of such a deep neural network architecture for feature learning and
extraction, we train deep convolutional StochasticNets to learn abstract
features using the CIFAR-10 dataset, and extract the learned features from
images to perform classification on the SVHN and STL-10 datasets. Experimental
results show that features learned using deep convolutional StochasticNets,
with fewer neural connections than conventional deep convolutional neural
networks, can allow for better or comparable classification accuracy than
conventional deep neural networks: relative test error decrease of ~4.5% for
classification on the STL-10 dataset and ~1% for classification on the SVHN
dataset. Furthermore, it was shown that the deep features extracted using deep
convolutional StochasticNets can provide comparable classification accuracy
even when only 10% of the training data is used for feature learning. Finally,
it was also shown that significant gains in feature extraction speed can be
achieved in embedded applications using StochasticNets. As such, StochasticNets
allow for faster feature learning and extraction performance while facilitate
for better or comparable accuracy performances.
| 210
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|
Theoretical Models of Learning to Learn
|
A Machine can only learn if it is biased in some way. Typically the bias is
supplied by hand, for example through the choice of an appropriate set of
features. However, if the learning machine is embedded within an {\em
environment} of related tasks, then it can {\em learn} its own bias by learning
sufficiently many tasks from the environment. In this paper two models of bias
learning (or equivalently, learning to learn) are introduced and the main
theoretical results presented. The first model is a PAC-type model based on
empirical process theory, while the second is a hierarchical Bayes model.
|
Disliked
|
Theoretical Models of Learning to Learn : A Machine can only learn if it is biased in some way. Typically the bias is
supplied by hand, for example through the choice of an appropriate set of
features. However, if the learning machine is embedded within an {\em
environment} of related tasks, then it can {\em learn} its own bias by learning
sufficiently many tasks from the environment. In this paper two models of bias
learning (or equivalently, learning to learn) are introduced and the main
theoretical results presented. The first model is a PAC-type model based on
empirical process theory, while the second is a hierarchical Bayes model.
| 0
|
[email protected] [SEP] Theoretical Models of Learning to Learn : A Machine can only learn if it is biased in some way. Typically the bias is
supplied by hand, for example through the choice of an appropriate set of
features. However, if the learning machine is embedded within an {\em
environment} of related tasks, then it can {\em learn} its own bias by learning
sufficiently many tasks from the environment. In this paper two models of bias
learning (or equivalently, learning to learn) are introduced and the main
theoretical results presented. The first model is a PAC-type model based on
empirical process theory, while the second is a hierarchical Bayes model.
| 64
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|
The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning
|
Machine learning is usually defined in behaviourist terms, where external
validation is the primary mechanism of learning. In this paper, I argue for a
more holistic interpretation in which finding more probable, efficient and
abstract representations is as central to learning as performance. In other
words, machine learning should be extended with strategies to reason over its
own learning process, leading to so-called meta-cognitive machine learning. As
such, the de facto definition of machine learning should be reformulated in
these intrinsically multi-objective terms, taking into account not only the
task performance but also internal learning objectives. To this end, we suggest
a "model entropy function" to be defined that quantifies the efficiency of the
internal learning processes. It is conjured that the minimization of this model
entropy leads to concept formation. Besides philosophical aspects, some initial
illustrations are included to support the claims.
|
Disliked
|
The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning : Machine learning is usually defined in behaviourist terms, where external
validation is the primary mechanism of learning. In this paper, I argue for a
more holistic interpretation in which finding more probable, efficient and
abstract representations is as central to learning as performance. In other
words, machine learning should be extended with strategies to reason over its
own learning process, leading to so-called meta-cognitive machine learning. As
such, the de facto definition of machine learning should be reformulated in
these intrinsically multi-objective terms, taking into account not only the
task performance but also internal learning objectives. To this end, we suggest
a "model entropy function" to be defined that quantifies the efficiency of the
internal learning processes. It is conjured that the minimization of this model
entropy leads to concept formation. Besides philosophical aspects, some initial
illustrations are included to support the claims.
| 0
|
[email protected] [SEP] The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning : Machine learning is usually defined in behaviourist terms, where external
validation is the primary mechanism of learning. In this paper, I argue for a
more holistic interpretation in which finding more probable, efficient and
abstract representations is as central to learning as performance. In other
words, machine learning should be extended with strategies to reason over its
own learning process, leading to so-called meta-cognitive machine learning. As
such, the de facto definition of machine learning should be reformulated in
these intrinsically multi-objective terms, taking into account not only the
task performance but also internal learning objectives. To this end, we suggest
a "model entropy function" to be defined that quantifies the efficiency of the
internal learning processes. It is conjured that the minimization of this model
entropy leads to concept formation. Besides philosophical aspects, some initial
illustrations are included to support the claims.
| 142
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High-Precise Robot Arm Manipulation based on Online Iterative Learning and Forward Simulation with Positioning Error Below End-Effector Physical Minimum Displacement
|
Precision is a crucial performance indicator for robot arms, as high
precision manipulation allows for a wider range of applications. Traditional
methods for improving robot arm precision rely on error compensation. However,
these methods are often not robust and lack adaptability. Learning-based
methods offer greater flexibility and adaptability, while current researches
show that they often fall short in achieving high precision and struggle to
handle many scenarios requiring high precision. In this paper, we propose a
novel high-precision robot arm manipulation framework based on online iterative
learning and forward simulation, which can achieve positioning error
(precision) less than end-effector physical minimum displacement. Additionally,
we parallelize multiple high-precision manipulation strategies to better
combine online iterative learning and forward simulation. Furthermore, we
consider the joint angular resolution of the real robot arm, which is usually
neglected in related works. A series of experiments on both simulation and real
UR3 robot arm platforms demonstrate that our proposed method is effective and
promising. The related code will be available soon.
|
Liked
|
High-Precise Robot Arm Manipulation based on Online Iterative Learning and Forward Simulation with Positioning Error Below End-Effector Physical Minimum Displacement : Precision is a crucial performance indicator for robot arms, as high
precision manipulation allows for a wider range of applications. Traditional
methods for improving robot arm precision rely on error compensation. However,
these methods are often not robust and lack adaptability. Learning-based
methods offer greater flexibility and adaptability, while current researches
show that they often fall short in achieving high precision and struggle to
handle many scenarios requiring high precision. In this paper, we propose a
novel high-precision robot arm manipulation framework based on online iterative
learning and forward simulation, which can achieve positioning error
(precision) less than end-effector physical minimum displacement. Additionally,
we parallelize multiple high-precision manipulation strategies to better
combine online iterative learning and forward simulation. Furthermore, we
consider the joint angular resolution of the real robot arm, which is usually
neglected in related works. A series of experiments on both simulation and real
UR3 robot arm platforms demonstrate that our proposed method is effective and
promising. The related code will be available soon.
| 1
|
[email protected] [SEP] High-Precise Robot Arm Manipulation based on Online Iterative Learning and Forward Simulation with Positioning Error Below End-Effector Physical Minimum Displacement : Precision is a crucial performance indicator for robot arms, as high
precision manipulation allows for a wider range of applications. Traditional
methods for improving robot arm precision rely on error compensation. However,
these methods are often not robust and lack adaptability. Learning-based
methods offer greater flexibility and adaptability, while current researches
show that they often fall short in achieving high precision and struggle to
handle many scenarios requiring high precision. In this paper, we propose a
novel high-precision robot arm manipulation framework based on online iterative
learning and forward simulation, which can achieve positioning error
(precision) less than end-effector physical minimum displacement. Additionally,
we parallelize multiple high-precision manipulation strategies to better
combine online iterative learning and forward simulation. Furthermore, we
consider the joint angular resolution of the real robot arm, which is usually
neglected in related works. A series of experiments on both simulation and real
UR3 robot arm platforms demonstrate that our proposed method is effective and
promising. The related code will be available soon.
| 423
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|
Mental Models of Adversarial Machine Learning
|
Although machine learning is widely used in practice, little is known about
practitioners' understanding of potential security challenges. In this work, we
close this substantial gap and contribute a qualitative study focusing on
developers' mental models of the machine learning pipeline and potentially
vulnerable components. Similar studies have helped in other security fields to
discover root causes or improve risk communication. Our study reveals two
\facets of practitioners' mental models of machine learning security. Firstly,
practitioners often confuse machine learning security with threats and defences
that are not directly related to machine learning. Secondly, in contrast to
most academic research, our participants perceive security of machine learning
as not solely related to individual models, but rather in the context of entire
workflows that consist of multiple components. Jointly with our additional
findings, these two facets provide a foundation to substantiate mental models
for machine learning security and have implications for the integration of
adversarial machine learning into corporate workflows, \new{decreasing
practitioners' reported uncertainty}, and appropriate regulatory frameworks for
machine learning security.
|
Disliked
|
Mental Models of Adversarial Machine Learning : Although machine learning is widely used in practice, little is known about
practitioners' understanding of potential security challenges. In this work, we
close this substantial gap and contribute a qualitative study focusing on
developers' mental models of the machine learning pipeline and potentially
vulnerable components. Similar studies have helped in other security fields to
discover root causes or improve risk communication. Our study reveals two
\facets of practitioners' mental models of machine learning security. Firstly,
practitioners often confuse machine learning security with threats and defences
that are not directly related to machine learning. Secondly, in contrast to
most academic research, our participants perceive security of machine learning
as not solely related to individual models, but rather in the context of entire
workflows that consist of multiple components. Jointly with our additional
findings, these two facets provide a foundation to substantiate mental models
for machine learning security and have implications for the integration of
adversarial machine learning into corporate workflows, \new{decreasing
practitioners' reported uncertainty}, and appropriate regulatory frameworks for
machine learning security.
| 0
|
[email protected] [SEP] Mental Models of Adversarial Machine Learning : Although machine learning is widely used in practice, little is known about
practitioners' understanding of potential security challenges. In this work, we
close this substantial gap and contribute a qualitative study focusing on
developers' mental models of the machine learning pipeline and potentially
vulnerable components. Similar studies have helped in other security fields to
discover root causes or improve risk communication. Our study reveals two
\facets of practitioners' mental models of machine learning security. Firstly,
practitioners often confuse machine learning security with threats and defences
that are not directly related to machine learning. Secondly, in contrast to
most academic research, our participants perceive security of machine learning
as not solely related to individual models, but rather in the context of entire
workflows that consist of multiple components. Jointly with our additional
findings, these two facets provide a foundation to substantiate mental models
for machine learning security and have implications for the integration of
adversarial machine learning into corporate workflows, \new{decreasing
practitioners' reported uncertainty}, and appropriate regulatory frameworks for
machine learning security.
| 111
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|
Introduction to deep learning
|
Deep Learning (DL) has made a major impact on data science in the last
decade. This chapter introduces the basic concepts of this field. It includes
both the basic structures used to design deep neural networks and a brief
survey of some of its popular use cases.
|
Disliked
|
Introduction to deep learning : Deep Learning (DL) has made a major impact on data science in the last
decade. This chapter introduces the basic concepts of this field. It includes
both the basic structures used to design deep neural networks and a brief
survey of some of its popular use cases.
| 0
|
[email protected] [SEP] Introduction to deep learning : Deep Learning (DL) has made a major impact on data science in the last
decade. This chapter introduces the basic concepts of this field. It includes
both the basic structures used to design deep neural networks and a brief
survey of some of its popular use cases.
| 201
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|
Rethinking Causal Mask Attention for Vision-Language Inference
|
Causal attention has become a foundational mechanism in autoregressive
vision-language models (VLMs), unifying textual and visual inputs under a
single generative framework. However, existing causal mask-based strategies are
inherited from large language models (LLMs) where they are tailored for
text-only decoding, and their adaptation to vision tokens is insufficiently
addressed in the prefill stage. Strictly masking future positions for vision
queries introduces overly rigid constraints, which hinder the model's ability
to leverage future context that often contains essential semantic cues for
accurate inference. In this work, we empirically investigate how different
causal masking strategies affect vision-language inference and then propose a
family of future-aware attentions tailored for this setting. We first
empirically analyze the effect of previewing future tokens for vision queries
and demonstrate that rigid masking undermines the model's capacity to capture
useful contextual semantic representations. Based on these findings, we propose
a lightweight attention family that aggregates future visual context into past
representations via pooling, effectively preserving the autoregressive
structure while enhancing cross-token dependencies. We evaluate a range of
causal masks across diverse vision-language inference settings and show that
selectively compressing future semantic context into past representations
benefits the inference.
|
Liked
|
Rethinking Causal Mask Attention for Vision-Language Inference : Causal attention has become a foundational mechanism in autoregressive
vision-language models (VLMs), unifying textual and visual inputs under a
single generative framework. However, existing causal mask-based strategies are
inherited from large language models (LLMs) where they are tailored for
text-only decoding, and their adaptation to vision tokens is insufficiently
addressed in the prefill stage. Strictly masking future positions for vision
queries introduces overly rigid constraints, which hinder the model's ability
to leverage future context that often contains essential semantic cues for
accurate inference. In this work, we empirically investigate how different
causal masking strategies affect vision-language inference and then propose a
family of future-aware attentions tailored for this setting. We first
empirically analyze the effect of previewing future tokens for vision queries
and demonstrate that rigid masking undermines the model's capacity to capture
useful contextual semantic representations. Based on these findings, we propose
a lightweight attention family that aggregates future visual context into past
representations via pooling, effectively preserving the autoregressive
structure while enhancing cross-token dependencies. We evaluate a range of
causal masks across diverse vision-language inference settings and show that
selectively compressing future semantic context into past representations
benefits the inference.
| 1
|
[email protected] [SEP] Rethinking Causal Mask Attention for Vision-Language Inference : Causal attention has become a foundational mechanism in autoregressive
vision-language models (VLMs), unifying textual and visual inputs under a
single generative framework. However, existing causal mask-based strategies are
inherited from large language models (LLMs) where they are tailored for
text-only decoding, and their adaptation to vision tokens is insufficiently
addressed in the prefill stage. Strictly masking future positions for vision
queries introduces overly rigid constraints, which hinder the model's ability
to leverage future context that often contains essential semantic cues for
accurate inference. In this work, we empirically investigate how different
causal masking strategies affect vision-language inference and then propose a
family of future-aware attentions tailored for this setting. We first
empirically analyze the effect of previewing future tokens for vision queries
and demonstrate that rigid masking undermines the model's capacity to capture
useful contextual semantic representations. Based on these findings, we propose
a lightweight attention family that aggregates future visual context into past
representations via pooling, effectively preserving the autoregressive
structure while enhancing cross-token dependencies. We evaluate a range of
causal masks across diverse vision-language inference settings and show that
selectively compressing future semantic context into past representations
benefits the inference.
| 379
|
|
Asset Pricing and Deep Learning
|
Traditional machine learning methods have been widely studied in financial
innovation. My study focuses on the application of deep learning methods on
asset pricing. I investigate various deep learning methods for asset pricing,
especially for risk premia measurement. All models take the same set of
predictive signals (firm characteristics, systematic risks and macroeconomics).
I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep
learning methods, and figure out that RNNs with memory mechanism and attention
have the best performance in terms of predictivity. Furthermore, I demonstrate
large economic gains to investors using deep learning forecasts. The results of
my comparative experiments highlight the importance of domain knowledge and
financial theory when designing deep learning models. I also show return
prediction tasks bring new challenges to deep learning. The time varying
distribution causes distribution shift problem, which is essential for
financial time series prediction. I demonstrate that deep learning methods can
improve asset risk premium measurement. Due to the booming deep learning
studies, they can constantly promote the study of underlying financial
mechanisms behind asset pricing. I also propose a promising research method
that learning from data and figuring out the underlying economic mechanisms
through explainable artificial intelligence (AI) methods. My findings not only
justify the value of deep learning in blooming fintech development, but also
highlight their prospects and advantages over traditional machine learning
methods.
|
Liked
|
Asset Pricing and Deep Learning : Traditional machine learning methods have been widely studied in financial
innovation. My study focuses on the application of deep learning methods on
asset pricing. I investigate various deep learning methods for asset pricing,
especially for risk premia measurement. All models take the same set of
predictive signals (firm characteristics, systematic risks and macroeconomics).
I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep
learning methods, and figure out that RNNs with memory mechanism and attention
have the best performance in terms of predictivity. Furthermore, I demonstrate
large economic gains to investors using deep learning forecasts. The results of
my comparative experiments highlight the importance of domain knowledge and
financial theory when designing deep learning models. I also show return
prediction tasks bring new challenges to deep learning. The time varying
distribution causes distribution shift problem, which is essential for
financial time series prediction. I demonstrate that deep learning methods can
improve asset risk premium measurement. Due to the booming deep learning
studies, they can constantly promote the study of underlying financial
mechanisms behind asset pricing. I also propose a promising research method
that learning from data and figuring out the underlying economic mechanisms
through explainable artificial intelligence (AI) methods. My findings not only
justify the value of deep learning in blooming fintech development, but also
highlight their prospects and advantages over traditional machine learning
methods.
| 1
|
[email protected] [SEP] Asset Pricing and Deep Learning : Traditional machine learning methods have been widely studied in financial
innovation. My study focuses on the application of deep learning methods on
asset pricing. I investigate various deep learning methods for asset pricing,
especially for risk premia measurement. All models take the same set of
predictive signals (firm characteristics, systematic risks and macroeconomics).
I demonstrate high performance of all kinds of state-of-the-art (SOTA) deep
learning methods, and figure out that RNNs with memory mechanism and attention
have the best performance in terms of predictivity. Furthermore, I demonstrate
large economic gains to investors using deep learning forecasts. The results of
my comparative experiments highlight the importance of domain knowledge and
financial theory when designing deep learning models. I also show return
prediction tasks bring new challenges to deep learning. The time varying
distribution causes distribution shift problem, which is essential for
financial time series prediction. I demonstrate that deep learning methods can
improve asset risk premium measurement. Due to the booming deep learning
studies, they can constantly promote the study of underlying financial
mechanisms behind asset pricing. I also propose a promising research method
that learning from data and figuring out the underlying economic mechanisms
through explainable artificial intelligence (AI) methods. My findings not only
justify the value of deep learning in blooming fintech development, but also
highlight their prospects and advantages over traditional machine learning
methods.
| 228
|
|
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios
|
Human-vehicle cooperative driving has become the critical technology of
autonomous driving, which reduces the workload of human drivers. However, the
complex and uncertain road environments bring great challenges to the visual
perception of cooperative systems. And the perception characteristics of
autonomous driving differ from manual driving a lot. To enhance the visual
perception capability of human-vehicle cooperative driving, this paper proposed
a cooperative visual perception model. 506 images of complex road and traffic
scenarios were collected as the data source. Then this paper improved the
object detection algorithm of autonomous vehicles. The mean perception accuracy
of traffic elements reached 75.52%. By the image fusion method, the gaze points
of human drivers were fused with vehicles' monitoring screens. Results revealed
that cooperative visual perception could reflect the riskiest zone and predict
the trajectory of conflict objects more precisely. The findings can be applied
in improving the visual perception algorithms and providing accurate data for
planning and control.
|
Liked
|
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios : Human-vehicle cooperative driving has become the critical technology of
autonomous driving, which reduces the workload of human drivers. However, the
complex and uncertain road environments bring great challenges to the visual
perception of cooperative systems. And the perception characteristics of
autonomous driving differ from manual driving a lot. To enhance the visual
perception capability of human-vehicle cooperative driving, this paper proposed
a cooperative visual perception model. 506 images of complex road and traffic
scenarios were collected as the data source. Then this paper improved the
object detection algorithm of autonomous vehicles. The mean perception accuracy
of traffic elements reached 75.52%. By the image fusion method, the gaze points
of human drivers were fused with vehicles' monitoring screens. Results revealed
that cooperative visual perception could reflect the riskiest zone and predict
the trajectory of conflict objects more precisely. The findings can be applied
in improving the visual perception algorithms and providing accurate data for
planning and control.
| 1
|
[email protected] [SEP] Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios : Human-vehicle cooperative driving has become the critical technology of
autonomous driving, which reduces the workload of human drivers. However, the
complex and uncertain road environments bring great challenges to the visual
perception of cooperative systems. And the perception characteristics of
autonomous driving differ from manual driving a lot. To enhance the visual
perception capability of human-vehicle cooperative driving, this paper proposed
a cooperative visual perception model. 506 images of complex road and traffic
scenarios were collected as the data source. Then this paper improved the
object detection algorithm of autonomous vehicles. The mean perception accuracy
of traffic elements reached 75.52%. By the image fusion method, the gaze points
of human drivers were fused with vehicles' monitoring screens. Results revealed
that cooperative visual perception could reflect the riskiest zone and predict
the trajectory of conflict objects more precisely. The findings can be applied
in improving the visual perception algorithms and providing accurate data for
planning and control.
| 277
|
|
Boosting Deep Ensembles with Learning Rate Tuning
|
The Learning Rate (LR) has a high impact on deep learning training
performance. A common practice is to train a Deep Neural Network (DNN) multiple
times with different LR policies to find the optimal LR policy, which has been
widely recognized as a daunting and costly task. Moreover, multiple times of
DNN training has not been effectively utilized. In practice, often only the
optimal LR is adopted, which misses the opportunities to further enhance the
overall accuracy of the deep learning system and results in a huge waste of
both computing resources and training time. This paper presents a novel
framework, LREnsemble, to effectively leverage effective learning rate tuning
to boost deep ensemble performance. We make three original contributions.
First, we show that the LR tuning with different LR policies can produce highly
diverse DNNs, which can be supplied as base models for deep ensembles. Second,
we leverage different ensemble selection algorithms to identify high-quality
deep ensembles from the large pool of base models with significant accuracy
improvements over the best single base model. Third, we propose LREnsemble, a
framework that utilizes the synergy of LR tuning and deep ensemble techniques
to enhance deep learning performance. The experiments on multiple benchmark
datasets have demonstrated the effectiveness of LREnsemble, generating up to
2.34% accuracy improvements over well-optimized baselines.
|
Disliked
|
Boosting Deep Ensembles with Learning Rate Tuning : The Learning Rate (LR) has a high impact on deep learning training
performance. A common practice is to train a Deep Neural Network (DNN) multiple
times with different LR policies to find the optimal LR policy, which has been
widely recognized as a daunting and costly task. Moreover, multiple times of
DNN training has not been effectively utilized. In practice, often only the
optimal LR is adopted, which misses the opportunities to further enhance the
overall accuracy of the deep learning system and results in a huge waste of
both computing resources and training time. This paper presents a novel
framework, LREnsemble, to effectively leverage effective learning rate tuning
to boost deep ensemble performance. We make three original contributions.
First, we show that the LR tuning with different LR policies can produce highly
diverse DNNs, which can be supplied as base models for deep ensembles. Second,
we leverage different ensemble selection algorithms to identify high-quality
deep ensembles from the large pool of base models with significant accuracy
improvements over the best single base model. Third, we propose LREnsemble, a
framework that utilizes the synergy of LR tuning and deep ensemble techniques
to enhance deep learning performance. The experiments on multiple benchmark
datasets have demonstrated the effectiveness of LREnsemble, generating up to
2.34% accuracy improvements over well-optimized baselines.
| 0
|
[email protected] [SEP] Boosting Deep Ensembles with Learning Rate Tuning : The Learning Rate (LR) has a high impact on deep learning training
performance. A common practice is to train a Deep Neural Network (DNN) multiple
times with different LR policies to find the optimal LR policy, which has been
widely recognized as a daunting and costly task. Moreover, multiple times of
DNN training has not been effectively utilized. In practice, often only the
optimal LR is adopted, which misses the opportunities to further enhance the
overall accuracy of the deep learning system and results in a huge waste of
both computing resources and training time. This paper presents a novel
framework, LREnsemble, to effectively leverage effective learning rate tuning
to boost deep ensemble performance. We make three original contributions.
First, we show that the LR tuning with different LR policies can produce highly
diverse DNNs, which can be supplied as base models for deep ensembles. Second,
we leverage different ensemble selection algorithms to identify high-quality
deep ensembles from the large pool of base models with significant accuracy
improvements over the best single base model. Third, we propose LREnsemble, a
framework that utilizes the synergy of LR tuning and deep ensemble techniques
to enhance deep learning performance. The experiments on multiple benchmark
datasets have demonstrated the effectiveness of LREnsemble, generating up to
2.34% accuracy improvements over well-optimized baselines.
| 251
|
|
Deep Learning for Sentiment Analysis : A Survey
|
Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.
|
Liked
|
Deep Learning for Sentiment Analysis : A Survey : Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.
| 1
|
[email protected] [SEP] Deep Learning for Sentiment Analysis : A Survey : Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysis in recent years. This paper first gives an overview of deep
learning and then provides a comprehensive survey of its current applications
in sentiment analysis.
| 181
|
|
Deep Neural Networks - A Brief History
|
Introduction to deep neural networks and their history.
|
Disliked
|
Deep Neural Networks - A Brief History : Introduction to deep neural networks and their history.
| 0
|
[email protected] [SEP] Deep Neural Networks - A Brief History : Introduction to deep neural networks and their history.
| 353
|
|
Vehicle-to-Everything Cooperative Perception for Autonomous Driving
|
Achieving fully autonomous driving with enhanced safety and efficiency relies
on vehicle-to-everything cooperative perception, which enables vehicles to
share perception data, thereby enhancing situational awareness and overcoming
the limitations of the sensing ability of individual vehicles.
Vehicle-to-everything cooperative perception plays a crucial role in extending
the perception range, increasing detection accuracy, and supporting more robust
decision-making and control in complex environments. This paper provides a
comprehensive survey of recent developments in vehicle-to-everything
cooperative perception, introducing mathematical models that characterize the
perception process under different collaboration strategies. Key techniques for
enabling reliable perception sharing, such as agent selection, data alignment,
and feature fusion, are examined in detail. In addition, major challenges are
discussed, including differences in agents and models, uncertainty in
perception outputs, and the impact of communication constraints such as
transmission delay and data loss. The paper concludes by outlining promising
research directions, including privacy-preserving artificial intelligence
methods, collaborative intelligence, and integrated sensing frameworks to
support future advancements in vehicle-to-everything cooperative perception.
|
Liked
|
Vehicle-to-Everything Cooperative Perception for Autonomous Driving : Achieving fully autonomous driving with enhanced safety and efficiency relies
on vehicle-to-everything cooperative perception, which enables vehicles to
share perception data, thereby enhancing situational awareness and overcoming
the limitations of the sensing ability of individual vehicles.
Vehicle-to-everything cooperative perception plays a crucial role in extending
the perception range, increasing detection accuracy, and supporting more robust
decision-making and control in complex environments. This paper provides a
comprehensive survey of recent developments in vehicle-to-everything
cooperative perception, introducing mathematical models that characterize the
perception process under different collaboration strategies. Key techniques for
enabling reliable perception sharing, such as agent selection, data alignment,
and feature fusion, are examined in detail. In addition, major challenges are
discussed, including differences in agents and models, uncertainty in
perception outputs, and the impact of communication constraints such as
transmission delay and data loss. The paper concludes by outlining promising
research directions, including privacy-preserving artificial intelligence
methods, collaborative intelligence, and integrated sensing frameworks to
support future advancements in vehicle-to-everything cooperative perception.
| 1
|
[email protected] [SEP] Vehicle-to-Everything Cooperative Perception for Autonomous Driving : Achieving fully autonomous driving with enhanced safety and efficiency relies
on vehicle-to-everything cooperative perception, which enables vehicles to
share perception data, thereby enhancing situational awareness and overcoming
the limitations of the sensing ability of individual vehicles.
Vehicle-to-everything cooperative perception plays a crucial role in extending
the perception range, increasing detection accuracy, and supporting more robust
decision-making and control in complex environments. This paper provides a
comprehensive survey of recent developments in vehicle-to-everything
cooperative perception, introducing mathematical models that characterize the
perception process under different collaboration strategies. Key techniques for
enabling reliable perception sharing, such as agent selection, data alignment,
and feature fusion, are examined in detail. In addition, major challenges are
discussed, including differences in agents and models, uncertainty in
perception outputs, and the impact of communication constraints such as
transmission delay and data loss. The paper concludes by outlining promising
research directions, including privacy-preserving artificial intelligence
methods, collaborative intelligence, and integrated sensing frameworks to
support future advancements in vehicle-to-everything cooperative perception.
| 307
|
|
Proceedings of the 29th International Conference on Machine Learning (ICML-12)
|
This is an index to the papers that appear in the Proceedings of the 29th
International Conference on Machine Learning (ICML-12). The conference was held
in Edinburgh, Scotland, June 27th - July 3rd, 2012.
|
Disliked
|
Proceedings of the 29th International Conference on Machine Learning (ICML-12) : This is an index to the papers that appear in the Proceedings of the 29th
International Conference on Machine Learning (ICML-12). The conference was held
in Edinburgh, Scotland, June 27th - July 3rd, 2012.
| 0
|
[email protected] [SEP] Proceedings of the 29th International Conference on Machine Learning (ICML-12) : This is an index to the papers that appear in the Proceedings of the 29th
International Conference on Machine Learning (ICML-12). The conference was held
in Edinburgh, Scotland, June 27th - July 3rd, 2012.
| 58
|
|
A Review of the Convergence of 5G/6G Architecture and Deep Learning
|
The convergence of 5G architecture and deep learning has gained a lot of
research interests in both the fields of wireless communication and artificial
intelligence. This is because deep learning technologies have been identified
to be the potential driver of the 5G technologies, that make up the 5G
architecture. Hence, there have been extensive surveys on the convergence of 5G
architecture and deep learning. However, most of the existing survey papers
mainly focused on how deep learning can converge with a specific 5G technology,
thus, not covering the full spectrum of the 5G architecture. Although there is
a recent survey paper that appears to be robust, a review of that paper shows
that it is not well structured to specifically cover the convergence of deep
learning and the 5G technologies. Hence, this paper provides a robust overview
of the convergence of the key 5G technologies and deep learning. The challenges
faced by such convergence are discussed. In addition, a brief overview of the
future 6G architecture, and how it can converge with deep learning is also
discussed.
|
Liked
|
A Review of the Convergence of 5G/6G Architecture and Deep Learning : The convergence of 5G architecture and deep learning has gained a lot of
research interests in both the fields of wireless communication and artificial
intelligence. This is because deep learning technologies have been identified
to be the potential driver of the 5G technologies, that make up the 5G
architecture. Hence, there have been extensive surveys on the convergence of 5G
architecture and deep learning. However, most of the existing survey papers
mainly focused on how deep learning can converge with a specific 5G technology,
thus, not covering the full spectrum of the 5G architecture. Although there is
a recent survey paper that appears to be robust, a review of that paper shows
that it is not well structured to specifically cover the convergence of deep
learning and the 5G technologies. Hence, this paper provides a robust overview
of the convergence of the key 5G technologies and deep learning. The challenges
faced by such convergence are discussed. In addition, a brief overview of the
future 6G architecture, and how it can converge with deep learning is also
discussed.
| 1
|
[email protected] [SEP] A Review of the Convergence of 5G/6G Architecture and Deep Learning : The convergence of 5G architecture and deep learning has gained a lot of
research interests in both the fields of wireless communication and artificial
intelligence. This is because deep learning technologies have been identified
to be the potential driver of the 5G technologies, that make up the 5G
architecture. Hence, there have been extensive surveys on the convergence of 5G
architecture and deep learning. However, most of the existing survey papers
mainly focused on how deep learning can converge with a specific 5G technology,
thus, not covering the full spectrum of the 5G architecture. Although there is
a recent survey paper that appears to be robust, a review of that paper shows
that it is not well structured to specifically cover the convergence of deep
learning and the 5G technologies. Hence, this paper provides a robust overview
of the convergence of the key 5G technologies and deep learning. The challenges
faced by such convergence are discussed. In addition, a brief overview of the
future 6G architecture, and how it can converge with deep learning is also
discussed.
| 247
|
|
Bimanual crop manipulation for human-inspired robotic harvesting
|
Most existing robotic harvesters utilize a unimanual approach; a single arm
grasps the crop and detaches it, either via a detachment movement, or by
cutting its stem with a specially designed gripper/cutter end-effector.
However, such unimanual solutions cannot be applied for sensitive crops and
cluttered environments like grapes and a vineyard where obstacles may occlude
the stem and leave no space for the cutter's placement. In such cases, the
solution would require a bimanual robot in order to visually unveil the stem
and manipulate the grasped crop to create cutting affordances which is similar
to the practice used by humans. In this work, a dual-arm coordinated motion
control methodology for reaching a stem pre-cut state is proposed. The camera
equipped arm with the cutter is reaching the stem, unveiling it as much as
possible, while the second arm is moving the grasped crop towards the
surrounding free-space to facilitate its stem cutting. Lab experimentation on a
mock-up vine setup with a plastic grape cluster evaluates the proposed
methodology, involving two UR5e robotic arms and a RealSense D415 camera.
|
Liked
|
Bimanual crop manipulation for human-inspired robotic harvesting : Most existing robotic harvesters utilize a unimanual approach; a single arm
grasps the crop and detaches it, either via a detachment movement, or by
cutting its stem with a specially designed gripper/cutter end-effector.
However, such unimanual solutions cannot be applied for sensitive crops and
cluttered environments like grapes and a vineyard where obstacles may occlude
the stem and leave no space for the cutter's placement. In such cases, the
solution would require a bimanual robot in order to visually unveil the stem
and manipulate the grasped crop to create cutting affordances which is similar
to the practice used by humans. In this work, a dual-arm coordinated motion
control methodology for reaching a stem pre-cut state is proposed. The camera
equipped arm with the cutter is reaching the stem, unveiling it as much as
possible, while the second arm is moving the grasped crop towards the
surrounding free-space to facilitate its stem cutting. Lab experimentation on a
mock-up vine setup with a plastic grape cluster evaluates the proposed
methodology, involving two UR5e robotic arms and a RealSense D415 camera.
| 1
|
[email protected] [SEP] Bimanual crop manipulation for human-inspired robotic harvesting : Most existing robotic harvesters utilize a unimanual approach; a single arm
grasps the crop and detaches it, either via a detachment movement, or by
cutting its stem with a specially designed gripper/cutter end-effector.
However, such unimanual solutions cannot be applied for sensitive crops and
cluttered environments like grapes and a vineyard where obstacles may occlude
the stem and leave no space for the cutter's placement. In such cases, the
solution would require a bimanual robot in order to visually unveil the stem
and manipulate the grasped crop to create cutting affordances which is similar
to the practice used by humans. In this work, a dual-arm coordinated motion
control methodology for reaching a stem pre-cut state is proposed. The camera
equipped arm with the cutter is reaching the stem, unveiling it as much as
possible, while the second arm is moving the grasped crop towards the
surrounding free-space to facilitate its stem cutting. Lab experimentation on a
mock-up vine setup with a plastic grape cluster evaluates the proposed
methodology, involving two UR5e robotic arms and a RealSense D415 camera.
| 500
|
|
Quantum-enhanced machine learning
|
The emerging field of quantum machine learning has the potential to
substantially aid in the problems and scope of artificial intelligence. This is
only enhanced by recent successes in the field of classical machine learning.
In this work we propose an approach for the systematic treatment of machine
learning, from the perspective of quantum information. Our approach is general
and covers all three main branches of machine learning: supervised,
unsupervised and reinforcement learning. While quantum improvements in
supervised and unsupervised learning have been reported, reinforcement learning
has received much less attention. Within our approach, we tackle the problem of
quantum enhancements in reinforcement learning as well, and propose a
systematic scheme for providing improvements. As an example, we show that
quadratic improvements in learning efficiency, and exponential improvements in
performance over limited time periods, can be obtained for a broad class of
learning problems.
|
Disliked
|
Quantum-enhanced machine learning : The emerging field of quantum machine learning has the potential to
substantially aid in the problems and scope of artificial intelligence. This is
only enhanced by recent successes in the field of classical machine learning.
In this work we propose an approach for the systematic treatment of machine
learning, from the perspective of quantum information. Our approach is general
and covers all three main branches of machine learning: supervised,
unsupervised and reinforcement learning. While quantum improvements in
supervised and unsupervised learning have been reported, reinforcement learning
has received much less attention. Within our approach, we tackle the problem of
quantum enhancements in reinforcement learning as well, and propose a
systematic scheme for providing improvements. As an example, we show that
quadratic improvements in learning efficiency, and exponential improvements in
performance over limited time periods, can be obtained for a broad class of
learning problems.
| 0
|
[email protected] [SEP] Quantum-enhanced machine learning : The emerging field of quantum machine learning has the potential to
substantially aid in the problems and scope of artificial intelligence. This is
only enhanced by recent successes in the field of classical machine learning.
In this work we propose an approach for the systematic treatment of machine
learning, from the perspective of quantum information. Our approach is general
and covers all three main branches of machine learning: supervised,
unsupervised and reinforcement learning. While quantum improvements in
supervised and unsupervised learning have been reported, reinforcement learning
has received much less attention. Within our approach, we tackle the problem of
quantum enhancements in reinforcement learning as well, and propose a
systematic scheme for providing improvements. As an example, we show that
quadratic improvements in learning efficiency, and exponential improvements in
performance over limited time periods, can be obtained for a broad class of
learning problems.
| 130
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|
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds
|
Autonomous vehicles may make wrong decisions due to inaccurate detection and
recognition. Therefore, an intelligent vehicle can combine its own data with
that of other vehicles to enhance perceptive ability, and thus improve
detection accuracy and driving safety. However, multi-vehicle cooperative
perception requires the integration of real world scenes and the traffic of raw
sensor data exchange far exceeds the bandwidth of existing vehicular networks.
To the best our knowledge, we are the first to conduct a study on raw-data
level cooperative perception for enhancing the detection ability of
self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse
the sensor data collected from different positions and angles of connected
vehicles. A point cloud based 3D object detection method is proposed to work on
a diversity of aligned point clouds. Experimental results on KITTI and our
collected dataset show that the proposed system outperforms perception by
extending sensing area, improving detection accuracy and promoting augmented
results. Most importantly, we demonstrate it is possible to transmit point
clouds data for cooperative perception via existing vehicular network
technologies.
|
Liked
|
Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds : Autonomous vehicles may make wrong decisions due to inaccurate detection and
recognition. Therefore, an intelligent vehicle can combine its own data with
that of other vehicles to enhance perceptive ability, and thus improve
detection accuracy and driving safety. However, multi-vehicle cooperative
perception requires the integration of real world scenes and the traffic of raw
sensor data exchange far exceeds the bandwidth of existing vehicular networks.
To the best our knowledge, we are the first to conduct a study on raw-data
level cooperative perception for enhancing the detection ability of
self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse
the sensor data collected from different positions and angles of connected
vehicles. A point cloud based 3D object detection method is proposed to work on
a diversity of aligned point clouds. Experimental results on KITTI and our
collected dataset show that the proposed system outperforms perception by
extending sensing area, improving detection accuracy and promoting augmented
results. Most importantly, we demonstrate it is possible to transmit point
clouds data for cooperative perception via existing vehicular network
technologies.
| 1
|
[email protected] [SEP] Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds : Autonomous vehicles may make wrong decisions due to inaccurate detection and
recognition. Therefore, an intelligent vehicle can combine its own data with
that of other vehicles to enhance perceptive ability, and thus improve
detection accuracy and driving safety. However, multi-vehicle cooperative
perception requires the integration of real world scenes and the traffic of raw
sensor data exchange far exceeds the bandwidth of existing vehicular networks.
To the best our knowledge, we are the first to conduct a study on raw-data
level cooperative perception for enhancing the detection ability of
self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse
the sensor data collected from different positions and angles of connected
vehicles. A point cloud based 3D object detection method is proposed to work on
a diversity of aligned point clouds. Experimental results on KITTI and our
collected dataset show that the proposed system outperforms perception by
extending sensing area, improving detection accuracy and promoting augmented
results. Most importantly, we demonstrate it is possible to transmit point
clouds data for cooperative perception via existing vehicular network
technologies.
| 330
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|
Engineering problems in machine learning systems
|
Fatal accidents are a major issue hindering the wide acceptance of
safety-critical systems that employ machine learning and deep learning models,
such as automated driving vehicles. In order to use machine learning in a
safety-critical system, it is necessary to demonstrate the safety and security
of the system through engineering processes. However, thus far, no such widely
accepted engineering concepts or frameworks have been established for these
systems. The key to using a machine learning model in a deductively engineered
system is decomposing the data-driven training of machine learning models into
requirement, design, and verification, particularly for machine learning models
used in safety-critical systems. Simultaneously, open problems and relevant
technical fields are not organized in a manner that enables researchers to
select a theme and work on it. In this study, we identify, classify, and
explore the open problems in engineering (safety-critical) machine learning
systems --- that is, in terms of requirement, design, and verification of
machine learning models and systems --- as well as discuss related works and
research directions, using automated driving vehicles as an example. Our
results show that machine learning models are characterized by a lack of
requirements specification, lack of design specification, lack of
interpretability, and lack of robustness. We also perform a gap analysis on a
conventional system quality standard SQuARE with the characteristics of machine
learning models to study quality models for machine learning systems. We find
that a lack of requirements specification and lack of robustness have the
greatest impact on conventional quality models.
|
Liked
|
Engineering problems in machine learning systems : Fatal accidents are a major issue hindering the wide acceptance of
safety-critical systems that employ machine learning and deep learning models,
such as automated driving vehicles. In order to use machine learning in a
safety-critical system, it is necessary to demonstrate the safety and security
of the system through engineering processes. However, thus far, no such widely
accepted engineering concepts or frameworks have been established for these
systems. The key to using a machine learning model in a deductively engineered
system is decomposing the data-driven training of machine learning models into
requirement, design, and verification, particularly for machine learning models
used in safety-critical systems. Simultaneously, open problems and relevant
technical fields are not organized in a manner that enables researchers to
select a theme and work on it. In this study, we identify, classify, and
explore the open problems in engineering (safety-critical) machine learning
systems --- that is, in terms of requirement, design, and verification of
machine learning models and systems --- as well as discuss related works and
research directions, using automated driving vehicles as an example. Our
results show that machine learning models are characterized by a lack of
requirements specification, lack of design specification, lack of
interpretability, and lack of robustness. We also perform a gap analysis on a
conventional system quality standard SQuARE with the characteristics of machine
learning models to study quality models for machine learning systems. We find
that a lack of requirements specification and lack of robustness have the
greatest impact on conventional quality models.
| 1
|
[email protected] [SEP] Engineering problems in machine learning systems : Fatal accidents are a major issue hindering the wide acceptance of
safety-critical systems that employ machine learning and deep learning models,
such as automated driving vehicles. In order to use machine learning in a
safety-critical system, it is necessary to demonstrate the safety and security
of the system through engineering processes. However, thus far, no such widely
accepted engineering concepts or frameworks have been established for these
systems. The key to using a machine learning model in a deductively engineered
system is decomposing the data-driven training of machine learning models into
requirement, design, and verification, particularly for machine learning models
used in safety-critical systems. Simultaneously, open problems and relevant
technical fields are not organized in a manner that enables researchers to
select a theme and work on it. In this study, we identify, classify, and
explore the open problems in engineering (safety-critical) machine learning
systems --- that is, in terms of requirement, design, and verification of
machine learning models and systems --- as well as discuss related works and
research directions, using automated driving vehicles as an example. Our
results show that machine learning models are characterized by a lack of
requirements specification, lack of design specification, lack of
interpretability, and lack of robustness. We also perform a gap analysis on a
conventional system quality standard SQuARE with the characteristics of machine
learning models to study quality models for machine learning systems. We find
that a lack of requirements specification and lack of robustness have the
greatest impact on conventional quality models.
| 138
|
|
Deep Bayesian Active Learning with Image Data
|
Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of data. Recent advances in deep learning, on the other hand, are
notorious for their dependence on large amounts of data. Second, many AL
acquisition functions rely on model uncertainty, yet deep learning methods
rarely represent such model uncertainty. In this paper we combine recent
advances in Bayesian deep learning into the active learning framework in a
practical way. We develop an active learning framework for high dimensional
data, a task which has been extremely challenging so far, with very sparse
existing literature. Taking advantage of specialised models such as Bayesian
convolutional neural networks, we demonstrate our active learning techniques
with image data, obtaining a significant improvement on existing active
learning approaches. We demonstrate this on both the MNIST dataset, as well as
for skin cancer diagnosis from lesion images (ISIC2016 task).
|
Disliked
|
Deep Bayesian Active Learning with Image Data : Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of data. Recent advances in deep learning, on the other hand, are
notorious for their dependence on large amounts of data. Second, many AL
acquisition functions rely on model uncertainty, yet deep learning methods
rarely represent such model uncertainty. In this paper we combine recent
advances in Bayesian deep learning into the active learning framework in a
practical way. We develop an active learning framework for high dimensional
data, a task which has been extremely challenging so far, with very sparse
existing literature. Taking advantage of specialised models such as Bayesian
convolutional neural networks, we demonstrate our active learning techniques
with image data, obtaining a significant improvement on existing active
learning approaches. We demonstrate this on both the MNIST dataset, as well as
for skin cancer diagnosis from lesion images (ISIC2016 task).
| 0
|
[email protected] [SEP] Deep Bayesian Active Learning with Image Data : Even though active learning forms an important pillar of machine learning,
deep learning tools are not prevalent within it. Deep learning poses several
difficulties when used in an active learning setting. First, active learning
(AL) methods generally rely on being able to learn and update models from small
amounts of data. Recent advances in deep learning, on the other hand, are
notorious for their dependence on large amounts of data. Second, many AL
acquisition functions rely on model uncertainty, yet deep learning methods
rarely represent such model uncertainty. In this paper we combine recent
advances in Bayesian deep learning into the active learning framework in a
practical way. We develop an active learning framework for high dimensional
data, a task which has been extremely challenging so far, with very sparse
existing literature. Taking advantage of specialised models such as Bayesian
convolutional neural networks, we demonstrate our active learning techniques
with image data, obtaining a significant improvement on existing active
learning approaches. We demonstrate this on both the MNIST dataset, as well as
for skin cancer diagnosis from lesion images (ISIC2016 task).
| 197
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|
Interpretations of Deep Learning by Forests and Haar Wavelets
|
This paper presents a basic property of region dividing of ReLU (rectified
linear unit) deep learning when new layers are successively added, by which two
new perspectives of interpreting deep learning are given. The first is related
to decision trees and forests; we construct a deep learning structure
equivalent to a forest in classification abilities, which means that certain
kinds of ReLU deep learning can be considered as forests. The second
perspective is that Haar wavelet represented functions can be approximated by
ReLU deep learning with arbitrary precision; and then a general conclusion of
function approximation abilities of ReLU deep learning is given. Finally,
generalize some of the conclusions of ReLU deep learning to the case of
sigmoid-unit deep learning.
|
Liked
|
Interpretations of Deep Learning by Forests and Haar Wavelets : This paper presents a basic property of region dividing of ReLU (rectified
linear unit) deep learning when new layers are successively added, by which two
new perspectives of interpreting deep learning are given. The first is related
to decision trees and forests; we construct a deep learning structure
equivalent to a forest in classification abilities, which means that certain
kinds of ReLU deep learning can be considered as forests. The second
perspective is that Haar wavelet represented functions can be approximated by
ReLU deep learning with arbitrary precision; and then a general conclusion of
function approximation abilities of ReLU deep learning is given. Finally,
generalize some of the conclusions of ReLU deep learning to the case of
sigmoid-unit deep learning.
| 1
|
[email protected] [SEP] Interpretations of Deep Learning by Forests and Haar Wavelets : This paper presents a basic property of region dividing of ReLU (rectified
linear unit) deep learning when new layers are successively added, by which two
new perspectives of interpreting deep learning are given. The first is related
to decision trees and forests; we construct a deep learning structure
equivalent to a forest in classification abilities, which means that certain
kinds of ReLU deep learning can be considered as forests. The second
perspective is that Haar wavelet represented functions can be approximated by
ReLU deep learning with arbitrary precision; and then a general conclusion of
function approximation abilities of ReLU deep learning is given. Finally,
generalize some of the conclusions of ReLU deep learning to the case of
sigmoid-unit deep learning.
| 186
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|
Deep Causal Learning: Representation, Discovery and Inference
|
Causal learning has garnered significant attention in recent years because it
reveals the essential relationships that underpin phenomena and delineates the
mechanisms by which the world evolves. Nevertheless, traditional causal
learning methods face numerous challenges and limitations, including
high-dimensional, unstructured variables, combinatorial optimization problems,
unobserved confounders, selection biases, and estimation inaccuracies. Deep
causal learning, which leverages deep neural networks, offers innovative
insights and solutions for addressing these challenges. Although numerous deep
learning-based methods for causal discovery and inference have been proposed,
there remains a dearth of reviews examining the underlying mechanisms by which
deep learning can enhance causal learning. In this article, we comprehensively
review how deep learning can contribute to causal learning by tackling
traditional challenges across three key dimensions: representation, discovery,
and inference. We emphasize that deep causal learning is pivotal for advancing
the theoretical frontiers and broadening the practical applications of causal
science. We conclude by summarizing open issues and outlining potential
directions for future research.
|
Liked
|
Deep Causal Learning: Representation, Discovery and Inference : Causal learning has garnered significant attention in recent years because it
reveals the essential relationships that underpin phenomena and delineates the
mechanisms by which the world evolves. Nevertheless, traditional causal
learning methods face numerous challenges and limitations, including
high-dimensional, unstructured variables, combinatorial optimization problems,
unobserved confounders, selection biases, and estimation inaccuracies. Deep
causal learning, which leverages deep neural networks, offers innovative
insights and solutions for addressing these challenges. Although numerous deep
learning-based methods for causal discovery and inference have been proposed,
there remains a dearth of reviews examining the underlying mechanisms by which
deep learning can enhance causal learning. In this article, we comprehensively
review how deep learning can contribute to causal learning by tackling
traditional challenges across three key dimensions: representation, discovery,
and inference. We emphasize that deep causal learning is pivotal for advancing
the theoretical frontiers and broadening the practical applications of causal
science. We conclude by summarizing open issues and outlining potential
directions for future research.
| 1
|
[email protected] [SEP] Deep Causal Learning: Representation, Discovery and Inference : Causal learning has garnered significant attention in recent years because it
reveals the essential relationships that underpin phenomena and delineates the
mechanisms by which the world evolves. Nevertheless, traditional causal
learning methods face numerous challenges and limitations, including
high-dimensional, unstructured variables, combinatorial optimization problems,
unobserved confounders, selection biases, and estimation inaccuracies. Deep
causal learning, which leverages deep neural networks, offers innovative
insights and solutions for addressing these challenges. Although numerous deep
learning-based methods for causal discovery and inference have been proposed,
there remains a dearth of reviews examining the underlying mechanisms by which
deep learning can enhance causal learning. In this article, we comprehensively
review how deep learning can contribute to causal learning by tackling
traditional challenges across three key dimensions: representation, discovery,
and inference. We emphasize that deep causal learning is pivotal for advancing
the theoretical frontiers and broadening the practical applications of causal
science. We conclude by summarizing open issues and outlining potential
directions for future research.
| 248
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|
Concept-Oriented Deep Learning
|
Concepts are the foundation of human deep learning, understanding, and
knowledge integration and transfer. We propose concept-oriented deep learning
(CODL) which extends (machine) deep learning with concept representations and
conceptual understanding capability. CODL addresses some of the major
limitations of deep learning: interpretability, transferability, contextual
adaptation, and requirement for lots of labeled training data. We discuss the
major aspects of CODL including concept graph, concept representations, concept
exemplars, and concept representation learning systems supporting incremental
and continual learning.
|
Disliked
|
Concept-Oriented Deep Learning : Concepts are the foundation of human deep learning, understanding, and
knowledge integration and transfer. We propose concept-oriented deep learning
(CODL) which extends (machine) deep learning with concept representations and
conceptual understanding capability. CODL addresses some of the major
limitations of deep learning: interpretability, transferability, contextual
adaptation, and requirement for lots of labeled training data. We discuss the
major aspects of CODL including concept graph, concept representations, concept
exemplars, and concept representation learning systems supporting incremental
and continual learning.
| 0
|
[email protected] [SEP] Concept-Oriented Deep Learning : Concepts are the foundation of human deep learning, understanding, and
knowledge integration and transfer. We propose concept-oriented deep learning
(CODL) which extends (machine) deep learning with concept representations and
conceptual understanding capability. CODL addresses some of the major
limitations of deep learning: interpretability, transferability, contextual
adaptation, and requirement for lots of labeled training data. We discuss the
major aspects of CODL including concept graph, concept representations, concept
exemplars, and concept representation learning systems supporting incremental
and continual learning.
| 159
|
|
An Essay on Optimization Mystery of Deep Learning
|
Despite the huge empirical success of deep learning, theoretical
understanding of neural networks learning process is still lacking. This is the
reason, why some of its features seem "mysterious". We emphasize two mysteries
of deep learning: generalization mystery, and optimization mystery. In this
essay we review and draw connections between several selected works concerning
the latter.
|
Liked
|
An Essay on Optimization Mystery of Deep Learning : Despite the huge empirical success of deep learning, theoretical
understanding of neural networks learning process is still lacking. This is the
reason, why some of its features seem "mysterious". We emphasize two mysteries
of deep learning: generalization mystery, and optimization mystery. In this
essay we review and draw connections between several selected works concerning
the latter.
| 1
|
[email protected] [SEP] An Essay on Optimization Mystery of Deep Learning : Despite the huge empirical success of deep learning, theoretical
understanding of neural networks learning process is still lacking. This is the
reason, why some of its features seem "mysterious". We emphasize two mysteries
of deep learning: generalization mystery, and optimization mystery. In this
essay we review and draw connections between several selected works concerning
the latter.
| 211
|
|
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
|
Machine learning is driving development across many fields in science and
engineering. A simple and efficient programming language could accelerate
applications of machine learning in various fields. Currently, the programming
languages most commonly used to develop machine learning algorithms include
Python, MATLAB, and C/C ++. However, none of these languages well balance both
efficiency and simplicity. The Julia language is a fast, easy-to-use, and
open-source programming language that was originally designed for
high-performance computing, which can well balance the efficiency and
simplicity. This paper summarizes the related research work and developments in
the application of the Julia language in machine learning. It first surveys the
popular machine learning algorithms that are developed in the Julia language.
Then, it investigates applications of the machine learning algorithms
implemented with the Julia language. Finally, it discusses the open issues and
the potential future directions that arise in the use of the Julia language in
machine learning.
|
Disliked
|
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues : Machine learning is driving development across many fields in science and
engineering. A simple and efficient programming language could accelerate
applications of machine learning in various fields. Currently, the programming
languages most commonly used to develop machine learning algorithms include
Python, MATLAB, and C/C ++. However, none of these languages well balance both
efficiency and simplicity. The Julia language is a fast, easy-to-use, and
open-source programming language that was originally designed for
high-performance computing, which can well balance the efficiency and
simplicity. This paper summarizes the related research work and developments in
the application of the Julia language in machine learning. It first surveys the
popular machine learning algorithms that are developed in the Julia language.
Then, it investigates applications of the machine learning algorithms
implemented with the Julia language. Finally, it discusses the open issues and
the potential future directions that arise in the use of the Julia language in
machine learning.
| 0
|
[email protected] [SEP] Julia Language in Machine Learning: Algorithms, Applications, and Open Issues : Machine learning is driving development across many fields in science and
engineering. A simple and efficient programming language could accelerate
applications of machine learning in various fields. Currently, the programming
languages most commonly used to develop machine learning algorithms include
Python, MATLAB, and C/C ++. However, none of these languages well balance both
efficiency and simplicity. The Julia language is a fast, easy-to-use, and
open-source programming language that was originally designed for
high-performance computing, which can well balance the efficiency and
simplicity. This paper summarizes the related research work and developments in
the application of the Julia language in machine learning. It first surveys the
popular machine learning algorithms that are developed in the Julia language.
Then, it investigates applications of the machine learning algorithms
implemented with the Julia language. Finally, it discusses the open issues and
the potential future directions that arise in the use of the Julia language in
machine learning.
| 113
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Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection
|
Recent escalation in the field of computer vision underpins a huddle of
algorithms with the magnificent potential to unravel the information contained
within images. These computer vision algorithms are being practised in medical
image analysis and are transfiguring the perception and interpretation of
Imaging data. Among these algorithms, Vision Transformers are evolved as one of
the most contemporary and dominant architectures that are being used in the
field of computer vision. These are immensely utilized by a plenty of
researchers to perform new as well as former experiments. Here, in this article
we investigate the intersection of Vision Transformers and Medical images and
proffered an overview of various ViTs based frameworks that are being used by
different researchers in order to decipher the obstacles in Medical Computer
Vision. We surveyed the application of Vision transformers in different areas
of medical computer vision such as image-based disease classification,
anatomical structure segmentation, registration, region-based lesion Detection,
captioning, report generation, reconstruction using multiple medical imaging
modalities that greatly assist in medical diagnosis and hence treatment
process. Along with this, we also demystify several imaging modalities used in
Medical Computer Vision. Moreover, to get more insight and deeper
understanding, self-attention mechanism of transformers is also explained
briefly. Conclusively, we also put some light on available data sets, adopted
methodology, their performance measures, challenges and their solutions in form
of discussion. We hope that this review article will open future directions for
researchers in medical computer vision.
|
Liked
|
Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection : Recent escalation in the field of computer vision underpins a huddle of
algorithms with the magnificent potential to unravel the information contained
within images. These computer vision algorithms are being practised in medical
image analysis and are transfiguring the perception and interpretation of
Imaging data. Among these algorithms, Vision Transformers are evolved as one of
the most contemporary and dominant architectures that are being used in the
field of computer vision. These are immensely utilized by a plenty of
researchers to perform new as well as former experiments. Here, in this article
we investigate the intersection of Vision Transformers and Medical images and
proffered an overview of various ViTs based frameworks that are being used by
different researchers in order to decipher the obstacles in Medical Computer
Vision. We surveyed the application of Vision transformers in different areas
of medical computer vision such as image-based disease classification,
anatomical structure segmentation, registration, region-based lesion Detection,
captioning, report generation, reconstruction using multiple medical imaging
modalities that greatly assist in medical diagnosis and hence treatment
process. Along with this, we also demystify several imaging modalities used in
Medical Computer Vision. Moreover, to get more insight and deeper
understanding, self-attention mechanism of transformers is also explained
briefly. Conclusively, we also put some light on available data sets, adopted
methodology, their performance measures, challenges and their solutions in form
of discussion. We hope that this review article will open future directions for
researchers in medical computer vision.
| 1
|
[email protected] [SEP] Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection : Recent escalation in the field of computer vision underpins a huddle of
algorithms with the magnificent potential to unravel the information contained
within images. These computer vision algorithms are being practised in medical
image analysis and are transfiguring the perception and interpretation of
Imaging data. Among these algorithms, Vision Transformers are evolved as one of
the most contemporary and dominant architectures that are being used in the
field of computer vision. These are immensely utilized by a plenty of
researchers to perform new as well as former experiments. Here, in this article
we investigate the intersection of Vision Transformers and Medical images and
proffered an overview of various ViTs based frameworks that are being used by
different researchers in order to decipher the obstacles in Medical Computer
Vision. We surveyed the application of Vision transformers in different areas
of medical computer vision such as image-based disease classification,
anatomical structure segmentation, registration, region-based lesion Detection,
captioning, report generation, reconstruction using multiple medical imaging
modalities that greatly assist in medical diagnosis and hence treatment
process. Along with this, we also demystify several imaging modalities used in
Medical Computer Vision. Moreover, to get more insight and deeper
understanding, self-attention mechanism of transformers is also explained
briefly. Conclusively, we also put some light on available data sets, adopted
methodology, their performance measures, challenges and their solutions in form
of discussion. We hope that this review article will open future directions for
researchers in medical computer vision.
| 339
|
|
Soft Arm-Motor Thrust Characterization for a Pneumatically Actuated Soft Morphing Quadrotor
|
In this work, an experimental characterization of the configuration space of
a soft, pneumatically actuated morphing quadrotor is presented, with a focus on
precise thrust characterization of its flexible arms, considering the effect of
downwash. Unlike traditional quadrotors, the soft drone has pneumatically
actuated arms, introducing complex, nonlinear interactions between motor thrust
and arm deformation, which make precise control challenging. The silicone arms
are actuated using differential pressure to achieve flexibility and thus have a
variable workspace compared to their fixed counter-parts. The deflection of the
soft arms during compression and expansion is controlled throughout the flight.
However, in real time, the downwash from the motor attached at the tip of the
soft arm generates a significant and random disturbance on the arm. This
disturbance affects both the desired deflection of the arm and the overall
stability of the system. To address this factor, an experimental
characterization of the effect of downwash on the deflection angle of the arm
is conducted.
|
Liked
|
Soft Arm-Motor Thrust Characterization for a Pneumatically Actuated Soft Morphing Quadrotor : In this work, an experimental characterization of the configuration space of
a soft, pneumatically actuated morphing quadrotor is presented, with a focus on
precise thrust characterization of its flexible arms, considering the effect of
downwash. Unlike traditional quadrotors, the soft drone has pneumatically
actuated arms, introducing complex, nonlinear interactions between motor thrust
and arm deformation, which make precise control challenging. The silicone arms
are actuated using differential pressure to achieve flexibility and thus have a
variable workspace compared to their fixed counter-parts. The deflection of the
soft arms during compression and expansion is controlled throughout the flight.
However, in real time, the downwash from the motor attached at the tip of the
soft arm generates a significant and random disturbance on the arm. This
disturbance affects both the desired deflection of the arm and the overall
stability of the system. To address this factor, an experimental
characterization of the effect of downwash on the deflection angle of the arm
is conducted.
| 1
|
[email protected] [SEP] Soft Arm-Motor Thrust Characterization for a Pneumatically Actuated Soft Morphing Quadrotor : In this work, an experimental characterization of the configuration space of
a soft, pneumatically actuated morphing quadrotor is presented, with a focus on
precise thrust characterization of its flexible arms, considering the effect of
downwash. Unlike traditional quadrotors, the soft drone has pneumatically
actuated arms, introducing complex, nonlinear interactions between motor thrust
and arm deformation, which make precise control challenging. The silicone arms
are actuated using differential pressure to achieve flexibility and thus have a
variable workspace compared to their fixed counter-parts. The deflection of the
soft arms during compression and expansion is controlled throughout the flight.
However, in real time, the downwash from the motor attached at the tip of the
soft arm generates a significant and random disturbance on the arm. This
disturbance affects both the desired deflection of the arm and the overall
stability of the system. To address this factor, an experimental
characterization of the effect of downwash on the deflection angle of the arm
is conducted.
| 460
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|
Towards A Rigorous Science of Interpretable Machine Learning
|
As machine learning systems become ubiquitous, there has been a surge of
interest in interpretable machine learning: systems that provide explanation
for their outputs. These explanations are often used to qualitatively assess
other criteria such as safety or non-discrimination. However, despite the
interest in interpretability, there is very little consensus on what
interpretable machine learning is and how it should be measured. In this
position paper, we first define interpretability and describe when
interpretability is needed (and when it is not). Next, we suggest a taxonomy
for rigorous evaluation and expose open questions towards a more rigorous
science of interpretable machine learning.
|
Disliked
|
Towards A Rigorous Science of Interpretable Machine Learning : As machine learning systems become ubiquitous, there has been a surge of
interest in interpretable machine learning: systems that provide explanation
for their outputs. These explanations are often used to qualitatively assess
other criteria such as safety or non-discrimination. However, despite the
interest in interpretability, there is very little consensus on what
interpretable machine learning is and how it should be measured. In this
position paper, we first define interpretability and describe when
interpretability is needed (and when it is not). Next, we suggest a taxonomy
for rigorous evaluation and expose open questions towards a more rigorous
science of interpretable machine learning.
| 0
|
[email protected] [SEP] Towards A Rigorous Science of Interpretable Machine Learning : As machine learning systems become ubiquitous, there has been a surge of
interest in interpretable machine learning: systems that provide explanation
for their outputs. These explanations are often used to qualitatively assess
other criteria such as safety or non-discrimination. However, despite the
interest in interpretability, there is very little consensus on what
interpretable machine learning is and how it should be measured. In this
position paper, we first define interpretability and describe when
interpretability is needed (and when it is not). Next, we suggest a taxonomy
for rigorous evaluation and expose open questions towards a more rigorous
science of interpretable machine learning.
| 71
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|
Transfer Learning for Voice Activity Detection: A Denoising Deep Neural Network Perspective
|
Mismatching problem between the source and target noisy corpora severely
hinder the practical use of the machine-learning-based voice activity detection
(VAD). In this paper, we try to address this problem in the transfer learning
prospective. Transfer learning tries to find a common learning machine or a
common feature subspace that is shared by both the source corpus and the target
corpus. The denoising deep neural network is used as the learning machine.
Three transfer techniques, which aim to learn common feature representations,
are used for analysis. Experimental results demonstrate the effectiveness of
the transfer learning schemes on the mismatch problem.
|
Disliked
|
Transfer Learning for Voice Activity Detection: A Denoising Deep Neural Network Perspective : Mismatching problem between the source and target noisy corpora severely
hinder the practical use of the machine-learning-based voice activity detection
(VAD). In this paper, we try to address this problem in the transfer learning
prospective. Transfer learning tries to find a common learning machine or a
common feature subspace that is shared by both the source corpus and the target
corpus. The denoising deep neural network is used as the learning machine.
Three transfer techniques, which aim to learn common feature representations,
are used for analysis. Experimental results demonstrate the effectiveness of
the transfer learning schemes on the mismatch problem.
| 0
|
[email protected] [SEP] Transfer Learning for Voice Activity Detection: A Denoising Deep Neural Network Perspective : Mismatching problem between the source and target noisy corpora severely
hinder the practical use of the machine-learning-based voice activity detection
(VAD). In this paper, we try to address this problem in the transfer learning
prospective. Transfer learning tries to find a common learning machine or a
common feature subspace that is shared by both the source corpus and the target
corpus. The denoising deep neural network is used as the learning machine.
Three transfer techniques, which aim to learn common feature representations,
are used for analysis. Experimental results demonstrate the effectiveness of
the transfer learning schemes on the mismatch problem.
| 149
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|
Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots
|
The interest in exploring planetary bodies for scientific investigation and
in-situ resource utilization is ever-rising. Yet, many sites of interest are
inaccessible to state-of-the-art planetary exploration robots because of the
robots' inability to traverse steep slopes, unstructured terrain, and loose
soil. Additionally, current single-robot approaches only allow a limited
exploration speed and a single set of skills. Here, we present a team of legged
robots with complementary skills for exploration missions in challenging
planetary analog environments. We equipped the robots with an efficient
locomotion controller, a mapping pipeline for online and post-mission
visualization, instance segmentation to highlight scientific targets, and
scientific instruments for remote and in-situ investigation. Furthermore, we
integrated a robotic arm on one of the robots to enable high-precision
measurements. Legged robots can swiftly navigate representative terrains, such
as granular slopes beyond 25 degrees, loose soil, and unstructured terrain,
highlighting their advantages compared to wheeled rover systems. We
successfully verified the approach in analog deployments at the BeyondGravity
ExoMars rover testbed, in a quarry in Switzerland, and at the Space Resources
Challenge in Luxembourg. Our results show that a team of legged robots with
advanced locomotion, perception, and measurement skills, as well as task-level
autonomy, can conduct successful, effective missions in a short time. Our
approach enables the scientific exploration of planetary target sites that are
currently out of human and robotic reach.
|
Disliked
|
Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots : The interest in exploring planetary bodies for scientific investigation and
in-situ resource utilization is ever-rising. Yet, many sites of interest are
inaccessible to state-of-the-art planetary exploration robots because of the
robots' inability to traverse steep slopes, unstructured terrain, and loose
soil. Additionally, current single-robot approaches only allow a limited
exploration speed and a single set of skills. Here, we present a team of legged
robots with complementary skills for exploration missions in challenging
planetary analog environments. We equipped the robots with an efficient
locomotion controller, a mapping pipeline for online and post-mission
visualization, instance segmentation to highlight scientific targets, and
scientific instruments for remote and in-situ investigation. Furthermore, we
integrated a robotic arm on one of the robots to enable high-precision
measurements. Legged robots can swiftly navigate representative terrains, such
as granular slopes beyond 25 degrees, loose soil, and unstructured terrain,
highlighting their advantages compared to wheeled rover systems. We
successfully verified the approach in analog deployments at the BeyondGravity
ExoMars rover testbed, in a quarry in Switzerland, and at the Space Resources
Challenge in Luxembourg. Our results show that a team of legged robots with
advanced locomotion, perception, and measurement skills, as well as task-level
autonomy, can conduct successful, effective missions in a short time. Our
approach enables the scientific exploration of planetary target sites that are
currently out of human and robotic reach.
| 0
|
[email protected] [SEP] Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots : The interest in exploring planetary bodies for scientific investigation and
in-situ resource utilization is ever-rising. Yet, many sites of interest are
inaccessible to state-of-the-art planetary exploration robots because of the
robots' inability to traverse steep slopes, unstructured terrain, and loose
soil. Additionally, current single-robot approaches only allow a limited
exploration speed and a single set of skills. Here, we present a team of legged
robots with complementary skills for exploration missions in challenging
planetary analog environments. We equipped the robots with an efficient
locomotion controller, a mapping pipeline for online and post-mission
visualization, instance segmentation to highlight scientific targets, and
scientific instruments for remote and in-situ investigation. Furthermore, we
integrated a robotic arm on one of the robots to enable high-precision
measurements. Legged robots can swiftly navigate representative terrains, such
as granular slopes beyond 25 degrees, loose soil, and unstructured terrain,
highlighting their advantages compared to wheeled rover systems. We
successfully verified the approach in analog deployments at the BeyondGravity
ExoMars rover testbed, in a quarry in Switzerland, and at the Space Resources
Challenge in Luxembourg. Our results show that a team of legged robots with
advanced locomotion, perception, and measurement skills, as well as task-level
autonomy, can conduct successful, effective missions in a short time. Our
approach enables the scientific exploration of planetary target sites that are
currently out of human and robotic reach.
| 548
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|
PAPRAS: Plug-And-Play Robotic Arm System
|
This paper presents a novel robotic arm system, named PAPRAS (Plug-And-Play
Robotic Arm System). PAPRAS consists of a portable robotic arm(s), docking
mount(s), and software architecture including a control system. By analyzing
the target task spaces at home, the dimensions and configuration of PAPRAS are
determined. PAPRAS's arm is light (less than 6kg) with an optimized 3D-printed
structure, and it has a high payload (3kg) as a human-arm-sized manipulator. A
locking mechanism is embedded in the structure for better portability and the
3D-printed docking mount can be installed easily. PAPRAS's software
architecture is developed on an open-source framework and optimized for
low-latency multiagent-based distributed manipulator control. A process to
create new demonstrations is presented to show PAPRAS's ease of use and
efficiency. In the paper, simulations and hardware experiments are presented in
various demonstrations, including sink-to-dishwasher manipulation, coffee
making, mobile manipulation on a quadruped, and suit-up demo to validate the
hardware and software design.
|
Liked
|
PAPRAS: Plug-And-Play Robotic Arm System : This paper presents a novel robotic arm system, named PAPRAS (Plug-And-Play
Robotic Arm System). PAPRAS consists of a portable robotic arm(s), docking
mount(s), and software architecture including a control system. By analyzing
the target task spaces at home, the dimensions and configuration of PAPRAS are
determined. PAPRAS's arm is light (less than 6kg) with an optimized 3D-printed
structure, and it has a high payload (3kg) as a human-arm-sized manipulator. A
locking mechanism is embedded in the structure for better portability and the
3D-printed docking mount can be installed easily. PAPRAS's software
architecture is developed on an open-source framework and optimized for
low-latency multiagent-based distributed manipulator control. A process to
create new demonstrations is presented to show PAPRAS's ease of use and
efficiency. In the paper, simulations and hardware experiments are presented in
various demonstrations, including sink-to-dishwasher manipulation, coffee
making, mobile manipulation on a quadruped, and suit-up demo to validate the
hardware and software design.
| 1
|
[email protected] [SEP] PAPRAS: Plug-And-Play Robotic Arm System : This paper presents a novel robotic arm system, named PAPRAS (Plug-And-Play
Robotic Arm System). PAPRAS consists of a portable robotic arm(s), docking
mount(s), and software architecture including a control system. By analyzing
the target task spaces at home, the dimensions and configuration of PAPRAS are
determined. PAPRAS's arm is light (less than 6kg) with an optimized 3D-printed
structure, and it has a high payload (3kg) as a human-arm-sized manipulator. A
locking mechanism is embedded in the structure for better portability and the
3D-printed docking mount can be installed easily. PAPRAS's software
architecture is developed on an open-source framework and optimized for
low-latency multiagent-based distributed manipulator control. A process to
create new demonstrations is presented to show PAPRAS's ease of use and
efficiency. In the paper, simulations and hardware experiments are presented in
various demonstrations, including sink-to-dishwasher manipulation, coffee
making, mobile manipulation on a quadruped, and suit-up demo to validate the
hardware and software design.
| 457
|
|
The configuration space of a robotic arm in a tunnel
|
We study the motion of a robotic arm inside a rectangular tunnel. We prove
that the configuration space of all possible positions of the robot is a CAT(0)
cubical complex. This allows us to use techniques from geometric group theory
to find the optimal way of moving the arm from one position to another. We also
compute the diameter of the configuration space, that is, the longest distance
between two positions of the robot.
|
Liked
|
The configuration space of a robotic arm in a tunnel : We study the motion of a robotic arm inside a rectangular tunnel. We prove
that the configuration space of all possible positions of the robot is a CAT(0)
cubical complex. This allows us to use techniques from geometric group theory
to find the optimal way of moving the arm from one position to another. We also
compute the diameter of the configuration space, that is, the longest distance
between two positions of the robot.
| 1
|
[email protected] [SEP] The configuration space of a robotic arm in a tunnel : We study the motion of a robotic arm inside a rectangular tunnel. We prove
that the configuration space of all possible positions of the robot is a CAT(0)
cubical complex. This allows us to use techniques from geometric group theory
to find the optimal way of moving the arm from one position to another. We also
compute the diameter of the configuration space, that is, the longest distance
between two positions of the robot.
| 472
|
|
An Anomaly Behavior Analysis Framework for Securing Autonomous Vehicle Perception
|
As a rapidly growing cyber-physical platform, Autonomous Vehicles (AVs) are
encountering more security challenges as their capabilities continue to expand.
In recent years, adversaries are actively targeting the perception sensors of
autonomous vehicles with sophisticated attacks that are not easily detected by
the vehicles' control systems. This work proposes an Anomaly Behavior Analysis
approach to detect a perception sensor attack against an autonomous vehicle.
The framework relies on temporal features extracted from a physics-based
autonomous vehicle behavior model to capture the normal behavior of vehicular
perception in autonomous driving. By employing a combination of model-based
techniques and machine learning algorithms, the proposed framework
distinguishes between normal and abnormal vehicular perception behavior. To
demonstrate the application of the framework in practice, we performed a depth
camera attack experiment on an autonomous vehicle testbed and generated an
extensive dataset. We validated the effectiveness of the proposed framework
using this real-world data and released the dataset for public access. To our
knowledge, this dataset is the first of its kind and will serve as a valuable
resource for the research community in evaluating their intrusion detection
techniques effectively.
|
Disliked
|
An Anomaly Behavior Analysis Framework for Securing Autonomous Vehicle Perception : As a rapidly growing cyber-physical platform, Autonomous Vehicles (AVs) are
encountering more security challenges as their capabilities continue to expand.
In recent years, adversaries are actively targeting the perception sensors of
autonomous vehicles with sophisticated attacks that are not easily detected by
the vehicles' control systems. This work proposes an Anomaly Behavior Analysis
approach to detect a perception sensor attack against an autonomous vehicle.
The framework relies on temporal features extracted from a physics-based
autonomous vehicle behavior model to capture the normal behavior of vehicular
perception in autonomous driving. By employing a combination of model-based
techniques and machine learning algorithms, the proposed framework
distinguishes between normal and abnormal vehicular perception behavior. To
demonstrate the application of the framework in practice, we performed a depth
camera attack experiment on an autonomous vehicle testbed and generated an
extensive dataset. We validated the effectiveness of the proposed framework
using this real-world data and released the dataset for public access. To our
knowledge, this dataset is the first of its kind and will serve as a valuable
resource for the research community in evaluating their intrusion detection
techniques effectively.
| 0
|
[email protected] [SEP] An Anomaly Behavior Analysis Framework for Securing Autonomous Vehicle Perception : As a rapidly growing cyber-physical platform, Autonomous Vehicles (AVs) are
encountering more security challenges as their capabilities continue to expand.
In recent years, adversaries are actively targeting the perception sensors of
autonomous vehicles with sophisticated attacks that are not easily detected by
the vehicles' control systems. This work proposes an Anomaly Behavior Analysis
approach to detect a perception sensor attack against an autonomous vehicle.
The framework relies on temporal features extracted from a physics-based
autonomous vehicle behavior model to capture the normal behavior of vehicular
perception in autonomous driving. By employing a combination of model-based
techniques and machine learning algorithms, the proposed framework
distinguishes between normal and abnormal vehicular perception behavior. To
demonstrate the application of the framework in practice, we performed a depth
camera attack experiment on an autonomous vehicle testbed and generated an
extensive dataset. We validated the effectiveness of the proposed framework
using this real-world data and released the dataset for public access. To our
knowledge, this dataset is the first of its kind and will serve as a valuable
resource for the research community in evaluating their intrusion detection
techniques effectively.
| 305
|
|
Continuous Collision Detection for a Robotic Arm Mounted on a Cable-Driven Parallel Robot
|
A continuous collision checking method for a cable-driven parallel robot with
an embarked robotic arm is proposed in this paper. The method aims at
validating paths by checking for collisions between any pair of robot bodies
(mobile platform, cables, and arm links). For a pair of bodies, an upper bound
on their relative velocity and a lower bound on the distance between the bodies
are computed and used to validate a portion of the path. These computations are
done repeatedly until a collision is found or the path is validated. The method
is integrated within the Humanoid Path Planner (HPP) software, tested with the
cable-driven parallel robot CoGiRo, and compared to a discretized validation
method.
|
Liked
|
Continuous Collision Detection for a Robotic Arm Mounted on a Cable-Driven Parallel Robot : A continuous collision checking method for a cable-driven parallel robot with
an embarked robotic arm is proposed in this paper. The method aims at
validating paths by checking for collisions between any pair of robot bodies
(mobile platform, cables, and arm links). For a pair of bodies, an upper bound
on their relative velocity and a lower bound on the distance between the bodies
are computed and used to validate a portion of the path. These computations are
done repeatedly until a collision is found or the path is validated. The method
is integrated within the Humanoid Path Planner (HPP) software, tested with the
cable-driven parallel robot CoGiRo, and compared to a discretized validation
method.
| 1
|
[email protected] [SEP] Continuous Collision Detection for a Robotic Arm Mounted on a Cable-Driven Parallel Robot : A continuous collision checking method for a cable-driven parallel robot with
an embarked robotic arm is proposed in this paper. The method aims at
validating paths by checking for collisions between any pair of robot bodies
(mobile platform, cables, and arm links). For a pair of bodies, an upper bound
on their relative velocity and a lower bound on the distance between the bodies
are computed and used to validate a portion of the path. These computations are
done repeatedly until a collision is found or the path is validated. The method
is integrated within the Humanoid Path Planner (HPP) software, tested with the
cable-driven parallel robot CoGiRo, and compared to a discretized validation
method.
| 528
|
|
Components of Machine Learning: Binding Bits and FLOPS
|
Many machine learning problems and methods are combinations of three
components: data, hypothesis space and loss function. Different machine
learning methods are obtained as combinations of different choices for the
representation of data, hypothesis space and loss function. After reviewing the
mathematical structure of these three components, we discuss intrinsic
trade-offs between statistical and computational properties of machine learning
methods.
|
Disliked
|
Components of Machine Learning: Binding Bits and FLOPS : Many machine learning problems and methods are combinations of three
components: data, hypothesis space and loss function. Different machine
learning methods are obtained as combinations of different choices for the
representation of data, hypothesis space and loss function. After reviewing the
mathematical structure of these three components, we discuss intrinsic
trade-offs between statistical and computational properties of machine learning
methods.
| 0
|
[email protected] [SEP] Components of Machine Learning: Binding Bits and FLOPS : Many machine learning problems and methods are combinations of three
components: data, hypothesis space and loss function. Different machine
learning methods are obtained as combinations of different choices for the
representation of data, hypothesis space and loss function. After reviewing the
mathematical structure of these three components, we discuss intrinsic
trade-offs between statistical and computational properties of machine learning
methods.
| 59
|
|
Arm Robot: AR-Enhanced Embodied Control and Visualization for Intuitive Robot Arm Manipulation
|
Embodied interaction has been introduced to human-robot interaction (HRI) as
a type of teleoperation, in which users control robot arms with bodily action
via handheld controllers or haptic gloves. Embodied teleoperation has made
robot control intuitive to non-technical users, but differences between humans'
and robots' capabilities \eg ranges of motion and response time, remain
challenging. In response, we present Arm Robot, an embodied robot arm
teleoperation system that helps users tackle human-robot discrepancies.
Specifically, Arm Robot (1) includes AR visualization as real-time feedback on
temporal and spatial discrepancies, and (2) allows users to change observing
perspectives and expand action space. We conducted a user study (N=18) to
investigate the usability of the Arm Robot and learn how users perceive the
embodiment. Our results show users could use Arm Robot's features to
effectively control the robot arm, providing insights for continued work in
embodied HRI.
|
Liked
|
Arm Robot: AR-Enhanced Embodied Control and Visualization for Intuitive Robot Arm Manipulation : Embodied interaction has been introduced to human-robot interaction (HRI) as
a type of teleoperation, in which users control robot arms with bodily action
via handheld controllers or haptic gloves. Embodied teleoperation has made
robot control intuitive to non-technical users, but differences between humans'
and robots' capabilities \eg ranges of motion and response time, remain
challenging. In response, we present Arm Robot, an embodied robot arm
teleoperation system that helps users tackle human-robot discrepancies.
Specifically, Arm Robot (1) includes AR visualization as real-time feedback on
temporal and spatial discrepancies, and (2) allows users to change observing
perspectives and expand action space. We conducted a user study (N=18) to
investigate the usability of the Arm Robot and learn how users perceive the
embodiment. Our results show users could use Arm Robot's features to
effectively control the robot arm, providing insights for continued work in
embodied HRI.
| 1
|
[email protected] [SEP] Arm Robot: AR-Enhanced Embodied Control and Visualization for Intuitive Robot Arm Manipulation : Embodied interaction has been introduced to human-robot interaction (HRI) as
a type of teleoperation, in which users control robot arms with bodily action
via handheld controllers or haptic gloves. Embodied teleoperation has made
robot control intuitive to non-technical users, but differences between humans'
and robots' capabilities \eg ranges of motion and response time, remain
challenging. In response, we present Arm Robot, an embodied robot arm
teleoperation system that helps users tackle human-robot discrepancies.
Specifically, Arm Robot (1) includes AR visualization as real-time feedback on
temporal and spatial discrepancies, and (2) allows users to change observing
perspectives and expand action space. We conducted a user study (N=18) to
investigate the usability of the Arm Robot and learn how users perceive the
embodiment. Our results show users could use Arm Robot's features to
effectively control the robot arm, providing insights for continued work in
embodied HRI.
| 386
|
|
Stochastic Variational Deep Kernel Learning
|
Deep kernel learning combines the non-parametric flexibility of kernel
methods with the inductive biases of deep learning architectures. We propose a
novel deep kernel learning model and stochastic variational inference procedure
which generalizes deep kernel learning approaches to enable classification,
multi-task learning, additive covariance structures, and stochastic gradient
training. Specifically, we apply additive base kernels to subsets of output
features from deep neural architectures, and jointly learn the parameters of
the base kernels and deep network through a Gaussian process marginal
likelihood objective. Within this framework, we derive an efficient form of
stochastic variational inference which leverages local kernel interpolation,
inducing points, and structure exploiting algebra. We show improved performance
over stand alone deep networks, SVMs, and state of the art scalable Gaussian
processes on several classification benchmarks, including an airline delay
dataset containing 6 million training points, CIFAR, and ImageNet.
|
Disliked
|
Stochastic Variational Deep Kernel Learning : Deep kernel learning combines the non-parametric flexibility of kernel
methods with the inductive biases of deep learning architectures. We propose a
novel deep kernel learning model and stochastic variational inference procedure
which generalizes deep kernel learning approaches to enable classification,
multi-task learning, additive covariance structures, and stochastic gradient
training. Specifically, we apply additive base kernels to subsets of output
features from deep neural architectures, and jointly learn the parameters of
the base kernels and deep network through a Gaussian process marginal
likelihood objective. Within this framework, we derive an efficient form of
stochastic variational inference which leverages local kernel interpolation,
inducing points, and structure exploiting algebra. We show improved performance
over stand alone deep networks, SVMs, and state of the art scalable Gaussian
processes on several classification benchmarks, including an airline delay
dataset containing 6 million training points, CIFAR, and ImageNet.
| 0
|
[email protected] [SEP] Stochastic Variational Deep Kernel Learning : Deep kernel learning combines the non-parametric flexibility of kernel
methods with the inductive biases of deep learning architectures. We propose a
novel deep kernel learning model and stochastic variational inference procedure
which generalizes deep kernel learning approaches to enable classification,
multi-task learning, additive covariance structures, and stochastic gradient
training. Specifically, we apply additive base kernels to subsets of output
features from deep neural architectures, and jointly learn the parameters of
the base kernels and deep network through a Gaussian process marginal
likelihood objective. Within this framework, we derive an efficient form of
stochastic variational inference which leverages local kernel interpolation,
inducing points, and structure exploiting algebra. We show improved performance
over stand alone deep networks, SVMs, and state of the art scalable Gaussian
processes on several classification benchmarks, including an airline delay
dataset containing 6 million training points, CIFAR, and ImageNet.
| 245
|
|
Towards Modular Machine Learning Solution Development: Benefits and Trade-offs
|
Machine learning technologies have demonstrated immense capabilities in
various domains. They play a key role in the success of modern businesses.
However, adoption of machine learning technologies has a lot of untouched
potential. Cost of developing custom machine learning solutions that solve
unique business problems is a major inhibitor to far-reaching adoption of
machine learning technologies. We recognize that the monolithic nature
prevalent in today's machine learning applications stands in the way of
efficient and cost effective customized machine learning solution development.
In this work we explore the benefits of modular machine learning solutions and
discuss how modular machine learning solutions can overcome some of the major
solution engineering limitations of monolithic machine learning solutions. We
analyze the trade-offs between modular and monolithic machine learning
solutions through three deep learning problems; one text based and the two
image based. Our experimental results show that modular machine learning
solutions have a promising potential to reap the solution engineering
advantages of modularity while gaining performance and data advantages in a way
the monolithic machine learning solutions do not permit.
|
Liked
|
Towards Modular Machine Learning Solution Development: Benefits and Trade-offs : Machine learning technologies have demonstrated immense capabilities in
various domains. They play a key role in the success of modern businesses.
However, adoption of machine learning technologies has a lot of untouched
potential. Cost of developing custom machine learning solutions that solve
unique business problems is a major inhibitor to far-reaching adoption of
machine learning technologies. We recognize that the monolithic nature
prevalent in today's machine learning applications stands in the way of
efficient and cost effective customized machine learning solution development.
In this work we explore the benefits of modular machine learning solutions and
discuss how modular machine learning solutions can overcome some of the major
solution engineering limitations of monolithic machine learning solutions. We
analyze the trade-offs between modular and monolithic machine learning
solutions through three deep learning problems; one text based and the two
image based. Our experimental results show that modular machine learning
solutions have a promising potential to reap the solution engineering
advantages of modularity while gaining performance and data advantages in a way
the monolithic machine learning solutions do not permit.
| 1
|
[email protected] [SEP] Towards Modular Machine Learning Solution Development: Benefits and Trade-offs : Machine learning technologies have demonstrated immense capabilities in
various domains. They play a key role in the success of modern businesses.
However, adoption of machine learning technologies has a lot of untouched
potential. Cost of developing custom machine learning solutions that solve
unique business problems is a major inhibitor to far-reaching adoption of
machine learning technologies. We recognize that the monolithic nature
prevalent in today's machine learning applications stands in the way of
efficient and cost effective customized machine learning solution development.
In this work we explore the benefits of modular machine learning solutions and
discuss how modular machine learning solutions can overcome some of the major
solution engineering limitations of monolithic machine learning solutions. We
analyze the trade-offs between modular and monolithic machine learning
solutions through three deep learning problems; one text based and the two
image based. Our experimental results show that modular machine learning
solutions have a promising potential to reap the solution engineering
advantages of modularity while gaining performance and data advantages in a way
the monolithic machine learning solutions do not permit.
| 25
|
|
Minimax deviation strategies for machine learning and recognition with short learning samples
|
The article is devoted to the problem of small learning samples in machine
learning. The flaws of maximum likelihood learning and minimax learning are
looked into and the concept of minimax deviation learning is introduced that is
free of those flaws.
|
Liked
|
Minimax deviation strategies for machine learning and recognition with short learning samples : The article is devoted to the problem of small learning samples in machine
learning. The flaws of maximum likelihood learning and minimax learning are
looked into and the concept of minimax deviation learning is introduced that is
free of those flaws.
| 1
|
[email protected] [SEP] Minimax deviation strategies for machine learning and recognition with short learning samples : The article is devoted to the problem of small learning samples in machine
learning. The flaws of maximum likelihood learning and minimax learning are
looked into and the concept of minimax deviation learning is introduced that is
free of those flaws.
| 1
|
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