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EurekAlert!7mon

D2-GCN: a graph convolutional network with dynamic disentanglement for node classification

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...
ascopubs.org1y

Annotations-free survival prediction with WSIs using graph convolutional neural networks.

Treatment patterns of metastatic urothelial cancer in the United States. Prediction of immune checkpoint inhibitor (ICI) response in patients with urothelial cancer (UC) by a novel RNA-based ICI ...

Yixin Zhou Advances Industrial Intelligence with AI-Based Anomaly Detection Model

A high-performance AI framework enhances anomaly detection in industrial systems using optimized Graph Deviation Networks and graph attention ...
JSTOR Daily1y

U-Net Convolutional Neural Network Model for Deep Red Tide Learning Using GOCI

Journal of Coastal Research, Special Issue #90: Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments (2019), pp. 302-309 (8 pages) GOCI launched in 2010 is a ...
ZDNet6y

Google ponders the shortcomings of machine learning

Critics of the current mode of artificial intelligence technology have grown louder in the last couple of years, and this week, Google, one of the biggest commercial beneficiaries of the current vogue ...
Semiconductor Engineering10mon

A graph placement methodology for fast chip design

“Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research1, chip floorplanning has defied automation, requiring months of ...