A New Type of Non-Standard High Performance DNN with Remarkable Stability – 
text
A New Type of Non-Standard High Performance DNN with Remarkable Stability – 
https://mltblog.com/3SA3OJ1
https://mltblog.com/3SA3OJ1
link
new_line
new_line
I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI. The original adaptive loss function introduced here for the first time, leads to spectacular performance improvements via a mechanism called equalization.
text
I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI. The original adaptive loss function introduced here for the first time, leads to spectacular performance improvements via a mechanism called equalization.
new_line
new_line
To accurately approximate any response, rather than connecting neurons with linear combinations and activation between layers, I use non-linear functions without activation, reducing the number of parameters, leading to explainability, easier fine tune, and faster training. The adaptive equalizer – a dynamical subsystem of its own – eliminates the linear part of the model, focusing on higher order interactions to accelerate convergence.
text
To accurately approximate any response, rather than connecting neurons with linear combinations and activation between layers, I use non-linear functions without activation, reducing the number of parameters, leading to explainability, easier fine tune, and faster training. The adaptive equalizer – a dynamical subsystem of its own – eliminates the linear part of the model, focusing on higher order interactions to accelerate convergence.
new_line
new_line
One example involves the Riemann zeta function. I exploit its well-known universality property to approximate any response. My system also handles singularities to deal with rare events or fraud detection. The loss function can be nowhere differentiable such as a Brownian motion. Many of the new discoveries are applicable to standard DNNs. Built from scratch, the Python code does not rely on any library other than Numpy. In particular, I do not use PyTorch, TensorFlow or Keras.
text
One example involves the Riemann zeta function. I exploit its well-known universality property to approximate any response. My system also handles singularities to deal with rare events or fraud detection. The loss function can be nowhere differentiable such as a Brownian motion. Many of the new discoveries are applicable to standard DNNs. Built from scratch, the Python code does not rely on any library other than Numpy. In particular, I do not use PyTorch, TensorFlow or Keras.
new_line
new_line
➡️ The PDF with many illustrations is available as paper 55, at 
text
➡️ The PDF with many illustrations is available as paper 55, at 
https://mltblog.com/3EQd2cA
https://mltblog.com/3EQd2cA
link
. It also features the replicable Python code (with link to GitHub), the data generated by the code, the theory, and various options including for evaluation.
text
. It also features the replicable Python code (with link to GitHub), the data generated by the code, the theory, and various options including for evaluation.
new_line
new_line
A New Type of Non-Standard High Performance DNN with Remarkable Stability – https://mltblog.com/3SA3OJ1

I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI. The original adaptive loss function introduced here for the first time, leads to spectacular performance improvements via a mechanism called equalization.

To accurately approximate any response, rather than connecting neurons with linear combinations and activation between layers, I use non-linear functions without activation, reducing the number of parameters, leading to explainability, easier fine tune, and faster training. The adaptive equalizer – a dynamical subsystem of its own – eliminates the linear part of the model, focusing on higher order interactions to accelerate convergence.

One example involves the Riemann zeta function. I exploit its well-known universality property to approximate any response. My system also handles singularities to deal with rare events or fraud detection. The loss function can be nowhere differentiable such as a Brownian motion. Many of the new discoveries are applicable to standard DNNs. Built from scratch, the Python code does not rely on any library other than Numpy. In particular, I do not use PyTorch, TensorFlow or Keras.

➡️ The PDF with many illustrations is available as paper 55, at https://mltblog.com/3EQd2cA. It also features the replicable Python code (with link to GitHub), the data generated by the code, the theory, and various options including for evaluation.


🚀
victor John6666