Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light ...

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

Recent research has employed chemical reaction networks (CRNs), which harness biochemical processes for computations that translate interactions involving biochemical species into graphical form.

Neuromorphic computing, as a novel approach to processing information by mimicking biological neural networks, has gradually demonstrated significant ...

Researchers at University of Southern California and University of Pennsylvania recently introduced a new nonlinear dynamical modeling framework based on recurrent neural networks (RNNs) that ...

Spin-wave networks may unlock energy-efficient AI computing. Discover the tech reshaping hardware demands for greener AI.