Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance. However, in new research published in APL Machine Learning, I have used a phenomenon ...

Neural networks are distributed computing structures inspired by the structure of a biological brain and aim to achieve cognitive performance comparable to that of humans but in a much shorter time.

Artificial intelligence systems that are designed with a biologically inspired architecture can simulate human brain activity ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

There was a time when purebred dogs were status symbols. Owners would crow about their German Shepherd's or Cocker Spaniel's impeccable pedigrees, sometimes boasting lineages that tread a single line ...

Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...

Physics-informed neural networks are faster and more accurate at predicting space junk trajectories than conventional methods, says Sierra Space. Credit: Alamy Stock Photo Sierra Space says it can ...

Forward-looking: Traditional computer chips perform tasks by sending digital signals at regular clock speeds, but new experimental hardware uses microwaves for specialized workloads. The resulting ...

The interaction of machine learning and optics/photonics is transforming the way we design new photonic structures, unearth latent physical laws, and develop intelligent photonic devices. Despite ...