Researchers at University of Southern California and University of Pennsylvania recently introduced a new nonlinear dynamical modeling framework based on recurrent neural networks (RNNs) that ...

Complex model architectures, demanding runtime computations, and transformer-specific operations introduce unique challenges.

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

This article will provide an overview of the most common neural network architectures -- including recurrent neural networks and convolutional neural -- and how they can be implemented to aid ...

Learn how to build your own GPT-style AI model with this step-by-step guide. Demystify large language models and unlock their ...

Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network–Based Artificial Intelligence Tool The best performing model, a Bi-LSTM NER ...

A new computational model improves estimation of Granger connectivity by removing spurious effects of external inputs, and estimation of linear encoding models by removing spurious effects of ...

A recurrent neural network-based framework to non-linearly model behaviorally relevant neural dynamics by Ingrid Fadelli, Phys.org ...