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A Blueprint for Using LLMs as Knowledge Tools: Humanities and Social Sciences

Uncurated, unstructured use of a Large Language Model in the humanities, social sciences, or arts reliably produces exactly ...
IEEE Spectrum on MSN

The classical advances needed to make quantum computers tick

Nvidia and others are developing new software, hardware, and AI to enable quantum ...
GitHub

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

The code in this repository implements an efficient generalization of the popular Convolutional Neural Networks (CNNs) to arbitrary graphs, presented in our paper: Michaël Defferrard, Xavier Bresson, ...
Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
IEEE

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...
Oak Ridge National Lab

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism...

Proceedings of The Eighth Annual Conference on Machine Learning and Systems Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their ...
Microsoft

xGCN An Extreme Graph Convolutional Network for Large-scale Social Link Prediction

\textit{Graph neural networks} (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and ...