Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and ...

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Artificial Intelligence systems powered by deep learning are changing how we work, communicate, and make decisions. If we ...

This study presents useful findings on how the transient absence of visual input (i.e., darkness) affects tactile neural encoding in the somatosensory cortex. The evidence supporting the authors' ...

This repository contains my implementation of a feed-forward neural network classifier in Python and Keras, trained on the Fashion-MNIST dataset. It closely follows the tutorial by The Clever ...

This repository implements a 3-layer neural network with L2 and Dropout regularization using Python and NumPy. It focuses on reducing overfitting and improving generalization.

Titans architecture complements attention layers with neural memory modules that select bits of information worth saving in the long term.

Operator learning is a transformative approach in scientific computing. It focuses on developing models that map functions to other functions, an essential aspect of solving partial differential ...

Spiking Neural Networks (SNNs) have been widely applied not only for their advantages in energy efficiency with discrete signal processing but also for their natural suitability to integrate ...

Automated neural architecture design is able to overcome these problems, but the most successful algorithms operate on significantly constrained design spaces, assuming the target network to consist ...