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

Knowledge graph completion aims to address knowledge gaps caused by difficulties in collecting real-world facts. This paper proposes a novel dual-view graph neural network model, DGE-ASR, which ...

Graphs are a powerful data structure for representing relational data, and Graph Neural Networks (GNNs) have emerged as effective tools for inference and learning on graph-structured data.

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 ...

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 ...

UB researchers are taking inspiration from the human brain to develop computing architecture that can support the growing ...

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