👉 Complete articles on Geometric Deep Learning, Graph Neural Networks, Topological Data Analysis with exercises are available on my Substack newsletter Hands-on Geometric Deep Learning The authors ...

OpenSTL is a comprehensive benchmark for spatio-temporal predictive learning, encompassing a broad spectrum of methods and diverse tasks, ranging from synthetic moving object trajectories to ...

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library ...

In the rapidly evolving field of artificial intelligence (AI), machine learning (ML) stands as a cornerstone, driving innovation across industries. Among the myriad of tools and frameworks available, ...

Composer is an open-source deep learning training library by MosaicML. Built on top of PyTorch, the Composer library makes it easier to implement distributed training workflows on large-scale clusters ...

State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian ...

A second obstacle relates to the development of new probabilistic models. From the perspective of developers, there are many necessary routines to implement in support of a probabilistic model, ...