Abstract stringlengths 663 1.82k | Keywords stringlengths 27 211 |
|---|---|
In this work we propose RELDEC, a novel approach for sequential decoding of moderate length low-density parity-check (LDPC) codes. The main idea behind RELDEC is that an optimized decoding policy is subsequently obtained via reinforcement learning based on a Markov decision process (MDP). In contrast to our previous wo... | sequential decoding;reinforcement learning;markov decision process |
Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained indivi... | universal image segmentation;multi-task train-once design;task-conditioned joint training strategy;query-text contrastive loss |
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. KG representation models should consider graph structures and text semantics, but no comprehensive open-sourced framework is mainly designed for KG regarding informative text description. In t... | knowledge graph;prompt learning;kg representation learning |
Ensuring security and integrity of elections constitutes an important challenge with wide-ranging societal implications. Classically, security guarantees can be ensured based on computational complexity, which may be challenged by quantum computers. We show that the use of quantum networks can enable information-theore... | quantum computer;quantum network;information theory;quantitative communication;communication complexity |
Brain tumor segmentation based on multi-modal magnetic resonance imaging (MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and postoperative evaluations. Despite the achieved inspiring performance by existing automatic segmentation methods, multi-modal MRI data are still unavailable in real-wor... | brain tumor segmentation;multi-modal magnetic resonance imaging;deeply supervIsed knowledge transfer network;knowledge transfer learning frame |
The task of Legal Statute Identification (LSI) aims to identify the legal statutes that are relevant to a given description of Facts or evidence of a legal case. Existing methods only utilize the textual content of Facts and legal articles to guide such a task. However, the citation network among case documents and leg... | legal statute identification task;citation network |
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms. A critical challenge is to accurately model user intent with only limited evidence in these short sessions. For example, is a flower bouquet being viewed meant as part of a wedding purchase o... | session-based recommendation system;recommender system;hypergraph attention network |
We combine neural rendering with multi-modal image and text representations to synthesize diverse 3D objects solely from natural language descriptions. Our method, Dream Fields, can generate the geometry and color of a wide range of objects without 3D supervision. Due to the scarcity of diverse, captioned 3D data, prio... | neural rendering;multi-modal image and text representations;synthesize diverse 3D objects;natural language descriptions |
Children's speech recognition is a vital, yet largely overlooked domain when building inclusive speech technologies. The major challenge impeding progress in this domain is the lack of adequate child speech corpora; however, recent advances in self-supervised learning have created a new opportunity for overcoming this ... | children's speech recognition;self-supervised adult speech representation;child speech corpora |
Although large language models have achieved impressive zero-shot ability, the huge model size generally incurs high cost. Recently, semi-parametric language models, which augment a smaller language model with an external retriever, have demonstrated promising language modeling capabilities. However, it remains unclear... | zero-shot generalization;semi-parametric language model;task-agnostic |
The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic,... | mental lexicon;cognitive multilayer network;cognitive network |
We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient an... | convolutional network architecture;semantic segmentation |
Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom k... | benchmark;knowledge-intensive language tasks |
A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants of contrastive learning, namely instance and class discrimination, targeting features that can discriminate between different instances for t... | out-of-distribution (OOD) detection;contrastive learning;effectiveness and limitation of existing contrastive learning methods for OOD detection |
Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization. This might cause harm to model's prediction... | review helpfulness prediction system;multimodal contrastive learning for multimodal review helpfulness prediction;mutual information between input modality;adaptive weighting scheme;multimodal Interaction module |
We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. The former offers an energy-efficient and event-driven paradigm for deep learning, while the latter has the ability to capture feature dependencies, enabling Transformer to achieve good performance. It ... | spiking neural network;self-attention mechanism;biological property |
Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the la... | liver tumor segmentation;u-net model;encoding feature supervised UNet++ |
Fitts' law is often employed as a predictive model for human movement, especially in the field of human-computer interaction. Models with an assumed Gaussian error structure are usually adequate when applied to data collected from controlled studies. However, observational data (often referred to as data gathered "in t... | fitts' law;two-component mixture structure;expectation-conditional-maximization |
The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find that this mechanism, while powerful and elegant, is not as important as typically thought for pretrained language models. We introduce PAPA,... | attention mechanism;probing method that replaces the input-dependent attention matrices with constant ones |
Out-of-Domain (OOD) intent detection is important for practical dialog systems. To alleviate the issue of lacking OOD training samples, some works propose synthesizing pseudo OOD samples and directly assigning one-hot OOD labels to these pseudo samples. However, these one-hot labels introduce noises to the training pro... | out-of-domain intent detection;adaptive soft pseudo labeling;co-training framework |
Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progres... | text embedding;massive text embedding benchmark |
Recent success of vision transformers has inspired a series of vision backbones with novel feature transformation paradigms, which report steady performance gain. Although the novel feature transformation designs are often claimed as the source of gain, some backbones may benefit from advanced engineering techniques, w... | vision transforme;identify real gain of popular convolution and attention operator;spatial feature aggregation |
Media has a substantial impact on the public perception of events. A one-sided or polarizing perspective on any topic is usually described as media bias. One of the ways how bias in news articles can be introduced is by altering word choice. Biased word choices are not always obvious, nor do they exhibit high context-d... | media;media bias detection;transformer-based deep learning architecture trained via multi-task Learning |
We propose Im2Wav, an image guided open-domain audio generation system. Given an input image or a sequence of images, Im2Wav generates a semantically relevant sound. Im2Wav is based on two Transformer language models, that operate over a hierarchical discrete audio representation obtained from a VQ-VAE based model. We ... | image guided open-domain audio generation system;image-to-audio model evaluation |
The pre-trained conversational models still fail to capture the implicit commonsense (CS) knowledge hidden in the dialogue interaction, even though they were pre-trained with an enormous dataset. In order to build a dialogue agent with CS capability, we firstly inject external knowledge into a pre-trained conversationa... | pre-trained conversational model;implicit commonsense (cs) knowledge;dialogue agent;external knowledge;two-way learning |
The practice of deep learning has shown that neural networks generalize remarkably well even with an extreme number of learned parameters. This appears to contradict traditional statistical wisdom, in which a trade-off between model complexity and fit to the data is essential. We set out to resolve this discrepancy fro... | deep learning;neural networks;convex optimization;sparse recovery;relu networks |
Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of differing growing condit... | machine learning;contrastive unpaired translation (CUT) generative adversarial network (GAN);image processing technique; |
This paper is on soft prompt learning for Vision \& Language (V&L) models. Similarly to their NLP counterparts, V\&L models can be adapted to a downstream task by learning soft continuous prompts using a few training examples. Current methods learn the soft prompts by minimizing a cross-entropy loss using as class weig... | soft prompt learning;cross entropy loss |
Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support ... | depressive disorder;semantic pipeline to study depression severity in individuals based on their social media writing;semantic ranking |
Event argument extraction has long been studied as a sequential prediction problem with extractive-based methods, tackling each argument in isolation. Although recent work proposes generation-based methods to capture cross-argument dependency, they require generating and post-processing a complicated target sequence (t... | event argument extraction;retrieval-augmented generative QA model |
Cross entropy loss has served as the main objective function for classification-based tasks. Widely deployed for learning neural network classifiers, it shows both effectiveness and a probabilistic interpretation. Recently, after the success of self supervised contrastive representation learning methods, supervised con... | cross entropy loss;self supervised contrastive representation learning method;learning both the representation and the classifier using one objective function |
This paper presents the design and development of multi-dialect automatic speech recognition for Arabic. Deep neural networks are becoming an effective tool to solve sequential data problems, particularly, adopting an end-to-end training of the system. Arabic speech recognition is a complex task because of the existenc... | multi-dialect automatic speech recognition;deep neural network;multi-dialectal corpus;acoustic model |
Recent Vision-Language Pre-trained (VLP) models based on dual encoder have attracted extensive attention from academia and industry due to their superior performance on various cross-modal tasks and high computational efficiency. They attempt to learn cross-modal representation using contrastive learning on image-text ... | vision-language pre-trained model;dual encoder;cross-modal representation;contrastive learning;multi-view contrastive learning |
In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse real-world applications. To improve the model capacity, besides designing aggregation operations, GNN topology design is also very important. In general, there are two mainstream GNN topology design manners. The first one is to sta... | graph neural network;feature selection and fusion strategy;topology design |
Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years. Despite some progress, existing methods are mostly limited to highly curated datasets (e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We argue that it is du... | pre-training;spatiotemporal representation learning;pretext task |
Gender classification algorithms have important applications in many domains today such as demographic research, law enforcement, as well as human-computer interaction. Recent research showed that algorithms trained on biased benchmark databases could result in algorithmic bias. However, to date, little research has be... | gender classification algorithms;benchmark database |
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical doma... | large-scale pre-trained vision language model;knowledge transferability;medical image domain;medical prompt;domain transfer capability |
Data is the fuel powering AI and creates tremendous value for many domains. However, collecting datasets for AI is a time-consuming, expensive, and complicated endeavor. For practitioners, data investment remains to be a leap of faith in practice. In this work, we study the data budgeting problem and formulate it as tw... | data;data budgeting problem |
Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoising 3D data, neither of which currently exist. In this work, we... | text-to-image synthesis;diffusion model;text-to-3D synthesis;probability density distillation |
We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The tra... | object detector;encoder-decoder pretraining and finetuning |
Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diff... | denoising diffusion model;comprehensive overview of diffusion models in the discipline of medical image analysis |
Real-time and human-interpretable decision-making in cyber-physical systems is a significant but challenging task, which usually requires predictions of possible future events from limited data. In this paper, we introduce a time-incremental learning framework: given a dataset of labeled signal traces with a common tim... | real-time and human-interpretable decision-making;time-incremental learning;decision tree |
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among t... | unsupervised learning;gaussian mixture model;multi-task learning;transfer learning |
Massively multilingual models pretrained on abundant corpora with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. In machine translation, multilingual pretrained models are often fine-tuned on parallel data from one or multiple language pairs. Multilingual fine-tu... | multilingual model;natural language processing;machine translation;language-family adapter |
Beyond identifying genetic variants, we introduce a set of Boolean relations that allows for a comprehensive classification of the relation for every pair of variants by taking all minimal alignments into account. We present an efficient algorithm to compute these relations, including a novel way of efficiently computi... | genetic variant;boolean relations |
The last decade's research in artificial intelligence had a significant impact on the advance of autonomous driving. Yet, safety remains a major concern when it comes to deploying such systems in high-risk environments. The objective of this thesis is to develop methodological tools which provide reliable uncertainty e... | artificial intelligence;autonomous driving;reliable uncertainty estimates for deep neural network;true class probability |
Low-frequency word prediction remains a challenge in modern neural machine translation (NMT) systems. Recent adaptive training methods promote the output of infrequent words by emphasizing their weights in the overall training objectives. Despite the improved recall of low-frequency words, their prediction precision is... | low frequency word prediction;neural machine translation;representation learning;token-level contrastive learning |
We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and developmen... | open-source deep learning library;deep learning;language-vision;language-vision tasks |
We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates audio samples conditioned on text inputs. AudioGen operates on a learnt discrete audio representation. The task of text-to-audio generation p... | generating audio sample;descriptive text caption;auto-regressive generative model |
We provide an operator-algebraic construction of integrable models of quantum field theory on 1+1 dimensional Minkowski space with fermionic scattering states. These are obtained by a grading of the wedge-local fields or, alternatively, of the underlying Borchers triple defining the theory. This leads to a net of grade... | operator-algebraic;quantum field theory;minkowski space |
- Downloads last month
- 34
Size of downloaded dataset files:
93.1 kB
Size of the auto-converted Parquet files:
93.1 kB
Number of rows:
90