Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily ...

Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Harvard University physicists have developed a simplified, physics-based mathematical model to better understand how neural ...

As research into photonic computing progresses, scientists seek to optimize the performance of optical computing devices by making purpose-specific changes to their design. A team led by Bo Wu and ...

“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 ...

Knowledge graph completion (KGC) aims to fill in missing entities and relations within knowledge graphs (KGs) to address their incompleteness. Most existing KGC models suffer from knowledge coverage ...

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 ...

The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end ...