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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 ...

Xuanrui Zhang Advances Financial Security Through Real-Time Machine Learning and Graph-Based Fraud Detection

A new framework integrates graph databases with real-time machine learning to enhance fraud detection and risk control in digital finance. By ...
ascopubs.org

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 ...
JSTOR Daily

Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings

As one of the key parts of rotary machine, the fault diagnosis and running condition monitoring of rolling bearings are of great importance for normal working and safe production of rotary machine.
OfficeChai

[Watch] 32-Year Old Yann LeCun Demonstrating Number Recognition AI System In 1993

Yann LeCun has become mainstream during the recent AI revolution in his role as the Chief AI Scientist at Meta, but he’s ...
ZDNet

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 ...