Adaptive method helps light-based quantum processors act more like neural networks

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language ...
Devdiscourse

AI’s growing role in cancer, rare disease and COVID-19 drug repurposing

For rare diseases, AI-driven repurposing fills a critical gap. With more than 7000 rare diseases and only a small percentage ...
The Lancet

Artificial intelligence in women’s cancers: innovation and challenges in clinical translation

Artificial intelligence (AI) is set to transform the care of women with cancer. From early detection via digital phenotyping ...

Xiaofei Han Publishes Groundbreaking Research on Multimodal Graph Neural Networks in Frontiers in Neuroscience

Frontiers in Neurorobotics has published a new paper by Xiaofei Han and Xin Dou introducing a next-generation artificial intelligence framework that combines graph neural networks and multimodal ...
IEEE

Finite-Size Convergence Bound of Graph Convolutional Networks by Graphon Analysis

Abstract: Graph Convolutional Networks (GCNs) prevail in the analysis of network-structured data, but how the graph size affects the performance is not fully understood. As the limit of graphs, the ...
IEEE

Segment-Based Graph Convolutional Network for Imputation of Missing Values Caused by Sensor Fault and Its Application in Aeroengines

Abstract: Graph neural networks (GNNs) have emerged as a popular solution for missing value imputation. Nevertheless, traditional GNNs consume a significant amount of memory when processing long time ...