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

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

Here’s an unusual concept: a computer-guided mechanical neural network (video, embedded below.) Why would one want a mechanical neural network? It’s essentially a tool to explore what it would take to ...

The Convolutional Neural Networks tutorial shows you how to build a small CNN for classifying CIFAR-10 images. You’ll want at least one GPU if you’re going to try this model—that will bring the ...

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...

Many "AI experts" have sprung up in the machine learning space since the advent of ChatGPT and other advanced generative AI constructs late last year, but Dr. James McCaffrey of Microsoft Research is ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

Artificial intelligence (AI) has made tremendous progress since its inception, and neural networks are usually part of that advancement. Neural networks that apply weights to variables in AI models ...

Can living neurons replace AI? A new study shows that biological neural networks (BNNs) can be trained to perform reservoir ...