The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new ...

A study from EPFL reveals why humans excel at recognizing objects from fragments while AI struggles, highlighting the ...

Understand how Highway Networks work and why they matter for training deep neural networks. A clear, beginner-friendly guide ...

CRISPR construct to genetically ablate the GABA transporter GAT3 in the mouse visual cortex, with effects on population-level neuronal activity. This work is important, as it sheds light on how GAT3 ...

We introduce the Geometric-DESIGNN method, which integrates Geometric Guidance with Directed Electrostatics Strategy within a Graph Neural Network framework to predict the stable configuration of ...

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.

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 ...

To build Neural Networks (NNs) on edge devices, Binarized Neural Network (BNN) has been proposed on the software side, while Computing-in-Memory (CiM) architecture has been proposed on the hardware ...

By tapping into a decades-old mathematical principle, researchers are hoping that Kolmogorov-Arnold networks will facilitate scientific discovery.