Tech Xplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
IEEE

Imagined Speech Decoding by Learning Consensus Graph From RKHS-Based Multi-View EEG Features

Abstract: Electroencephalogram (EEG)-based speech imagery has emerged as a novel brain–computer interface (BCI) paradigm, which holds promise for aiding individuals with speech disorders to achieve ...
IEEE

Multiple Orders Remaining Completion Time Prediction Based on Digital Twin and Spatiotemporal Multi-Graph Network Cascade Algorithm

Abstract: In discrete manufacturing systems, the concurrent production of customized orders increases the complexity of multi-order remaining completion time (MORCT) prediction. Most existing methods ...
GitHub

Rethinking Multi-Instance Learning through Graph-Driven Fusion: A Dual-Path Approach to Adaptive Representation

GDF-MIL (Graph-Driven Fusion Multiple Instance Learning) is a novel graph-driven multi-instance learning framework that adaptively balances topology modeling and semantic feature preservation through ...
GitHub

Geometric Graph U-Nets for Multi-Scale Protein Structure Modelling

Code for the paper "Multi-Scale Protein Structure Modelling with Geometric Graph U-Nets", by Chang Liu*, Vivian Li*, Linus Leong, Vladimir Radenkovic, Pietro Liò, and Chaitanya K. Joshi (*Equal ...