Fraunhofer researchers have developed a system that uses sensors and AI to monitor a driver's cognitive load. In the future, ...

Dive deep into the Muon Optimizer and learn how it enhances dense linear layers using the Newton-Schulz method combined with ...

Learn how the Inception Net V1 architecture works and how to implement it from scratch using PyTorch. Perfect for deep ...

Abstract: Photonic neural networks are emerging as promising computing platforms for artificial intelligence (AI). Particularly, integrated photonic unitary neural networks (IPUNNs) are capable of ...

This valuable study uses EEG and computational modeling to investigate hemispheric oscillatory asymmetries in unilateral spatial neglect. The work benefits from rare patient data and a careful ...

This study introduces Megabouts, a transformer-based classifier for larval zebrafish movement bouts. This useful tool is thoughtfully implemented and has clear potential to unify analyses across labs.

Why understanding AI’s layers is your competitive edge, and the three questions every IT leader must ask vendors.

How Afraid of the AI Apocalypse Should We Be? The A.I. researcher Eliezer Yudkowsky argues that we should be very afraid of artificial intelligence’s existential risks. This is an edited transcript of ...

Abstract: 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 ...

[1] F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, and G. Monfardini. The graph neural network model. IEEE Transactions on Neural Networks, 20(1):61 80, 2009.

This project contains implementations of simple neural network models, including training scripts for PyTorch and Lightning frameworks. The goal is to provide a modular, easy-to-understand codebase ...