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

Graph Neural Networks (GNNs) are widely used across fields, with inductive learning replacing transductive learning as the mainstream training paradigm due to its superior memory efficiency, ...

Graph Neural Networks (GNNs) have gained attention for their ability in capturing node interactions to generate node representations. However, their performances are frequently restricted in ...

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

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

A study published in npj Computational Materials presents a new AI system that uses computer vision and language processing ...

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

AI gaming platform Neural has announced their expansion to the Solana blockchain, marking a major milestone in the Creator ...

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 ...

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like telling a leaf apart from a rock. But they have struggled to build ...