GitHub

Internet-scale Neural Networks

Welcome! Bittensor is an open source platform on which you can produce competitive digital commodities. These digital commodities can be machine intelligence, storage space, compute power, protein ...
IEEE

End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial

Abstract: Cognitive diagnosis (CD) utilizes students' existing studying records to estimate their mastery of unknown knowledge concepts, which is vital for evaluating their learning abilities.
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
IEEE

Wasserstein Graph Neural Networks for Graphs With Missing Attributes

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
InfoWorld

Air-gapped Python: Setting up Python without a net(work)

Installing Python and related applications on a system without a network connection isn’t easy, but you can do it. Here’s how. The vast majority of modern software development revolves around one big ...
Scientific Research Publishing

Hamilton, W., Ying, Z. and Leskovec, J. (2017) Inductive Representation Learning on Large Graphs. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long ...

ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...