Neuroscientists have been trying to understand how the brain processes visual information for over a century. The development ...

A new technical paper titled “Solving sparse finite element problems on neuromorphic hardware” was published by researchers ...

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate ...

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning ...

When engineers build AI language models like GPT-5 from training data, at least two major processing features emerge: memorization (reciting exact text they’ve seen before, like famous quotes or ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

A low-dimensional voice latent space derived from deep learning captures speaker-identity representations in the temporal voice areas and supports reconstruction of voices preserving identity ...

Artificial intelligence might now be solving advanced math, performing complex reasoning, and even using personal computers, but today’s algorithms could still learn a thing or two from microscopic ...

Researchers have developed a hybrid CFD-neural network model for predicting TAIs in hydrogen-fueled turbines, improving ...