IEEE

Hyperspectral Image Classification via Dynamic Adaptive Graph Convolutional Network

Abstract: A dynamic graph convolutional network (DGCN) can represent temporal evolutionary features. Its compatibility with the spectral-dimensional characteristics of hyperspectral images (HSIs), ...
Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...

Deep learning reveals nanoparticle shape from routine tracking analysis without new hardware

Researchers at the University of Tokyo and the Innovation Center of NanoMedicine (iCONM) have developed an artificial ...
technologynetworks

Multiomics Data Integration With Machine Learning: Methods, Tools, and Biological Applications

Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...
AlphaGalileo

Crossing Virtual Boundaries: Rethinking Social Network Alignment via Graph Learning

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...
IEEE

Directed Acyclic Graph Convolutional Networks

Abstract: Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and the automated design of neural architectures. In this work, we ...
AlphaGalileo

Breakthrough in Efficient GNN Training Architecture: Research Team Proposes Hardware-Algorithm Co-Optimization Solution

A Chinese research team has achieved a breakthrough in improving the training efficiency of Graph Neural Networks (GNNs). They introduced an ...