Keane, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Paper 35037 (2026), ...

The application of neural network models to semiconductor device simulation has emerged as a transformative approach in the field of electronics. These models offer significant speed improvements over ...

Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...

Two University of Montana researchers are using advanced neural networks along with other things to improve climate models to help predict threats to coastal ar ...

Anthropic and OpenAI Group PBC are both preparing to release new flagship LLMs. According to Axios, Meta doesn’t expect its ...

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 ...

It might be futile to try making machines that are fully human. Our abilities have been refined through a lengthy evolution.

A research team at Tohoku University and Future University Hakodate has demonstrated that living biological neurons can be trained to perform a supervised temporal pattern learning task previously ...

Previously met with skepticism, AI won scientists a Nobel Prize for Chemistry in 2024 after they used it to solve the protein folding and design problem, and it has now been adopted by biologists ...

An improved model identifies power-reducing dust accumulation on photovoltaic modules, helping engineers know when the ...

A hunk of material bustles with electrons, one tickling another as they bop around. Quantifying how one particle jostles others in that scrum is so complicated that, beginning in the 1990s, physicists ...