Morning Overview on MSN
Physics-trained AI models speed engineering design and simulations
When engineers at Sumitomo Riko needed to speed up the design cycle for automotive rubber and polymer components, they turned ...
The idea of simplifying model weights isn’t a completely new one in AI research. For years, researchers have been experimenting with quantization techniques that squeeze their neural network weights ...
Keane, "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Paper 35037 (2026), ...
Morning Overview on MSN
Brain-inspired AI pruning boosts learning while shrinking model size
A human infant is born with roughly twice as many synapses as it will eventually need. Over the first few years of life, the ...
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 ...
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 ...
Researchers have developed several data-mechanism hybrid driven methods to improve key variables prediction in process ...
Researchers at Skoltech have proposed a new approach to training neural networks for wave propagation in absorbing media. The ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results