GitHub

Simplifying Graph Convolutional Networks

This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the ICML2019 paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and ...

Toward experiment-guided AlphaFold: Researchers overcome AI tool's single-conformation limitation

The AI-based program AlphaFold predicts a protein's 3D structure with remarkable accuracy. However, it tends to reduce heterogeneous structures to a single dominant conformation, or shape, and ...
IEEE

Self-Attention Graph Convolution Residual Network for Traffic Data Completion

Abstract: Complete and accurate traffic data is critical in urban traffic management, planning and operation. In fact, real-world traffic data contains missing values due to multiple factors, such as ...
IEEE

Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks

Abstract: The accurate short-term electric load forecasting (STLF) is critical for the safety and economical operation of modern electric power systems. Recently, the graph neural network (GNN) has ...
Frontiers

Optimizing large-scale graph database ingestion through edge value ranking: a proposed framework

1 Introduction Graph databases have become fundamental to modern enterprise applications, powering social network analysis, recommendation systems, fraud detection, and supply chain optimization. As ...
GitHub

matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples

Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
www.cs.cmu.edu

Tom Mitchell

For example, AI applications to medical diagnosis should be regulated very differently from AI applications to self-driving cars. U.S. National Academies report on AI and the Future of Work, study ...