Tech Xplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Medical Xpress

A recurrent neural network-based framework to non-linearly model behaviorally relevant neural dynamics

A key objective of several neuroscience studies is to understand and model how the dynamics of distinct populations of neurons give rise to specific human and animal behaviors. Many existing methods ...
Frontiers

Multi-modal Learning with Large-scale Models

Robot perception and cognition often rely on the integration of information from multiple sensory modalities, such as vision, ...

RRAM hasn't delivered yet, but stacked memory is being pitched to run neural networks in place

A team at the University of California, San Diego has redesigned how RRAM operates in an effort to accelerate the execution ...
Ars Technica

Here’s what’s really going on inside an LLM’s neural network

With most computer programs—even complex ones—you can meticulously trace through the code and memory usage to figure out why that program generates any specific behavior or output. That’s generally ...

Interpretability in deep learning for finance: A case study for the Heston model

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...