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

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...

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

In a new study published in Physical Review Letters, researchers used machine learning to discover multiple new classes of ...

LONDON, Feb 11 (Reuters) - An artificial neural network, a type of artificial intelligence which can engage in machine learning, can be patented, the United Kingdom's Supreme Court ruled on Wednesday ...

RIT researchers publish a paper in Nature Scientific Reports on a new tree-based machine learning algorithm used to predict chaos.

In the future, psychedelics may be used to treat addictions because they can shake up the brain. Along with therapy, ...

Wayve has launched GAIA-3, a generative foundation model for stress testing autonomous driving models. Aniruddha Kembhavi, Director of Science Strategy at Wayve, explains how this could advance ...

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 ...

In an interview with Technology Networks, Dr. Daniel Reker discusses how machine learning is improving data-scarce areas of ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...