Abstract: Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-dimensional ...
“He got it going," coach Sean Payton said of Nix after the game. "He started smiling and he started, you know, just all of that. Yeah, it was exciting to see. It just went back and forth. I said to ...
Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules ...
Samsung’s Now Bar can now show win probability information in sports matches. This takes the form of a graph showing which team is more likely to win the match. The feature is apparently rolling out ...
Abstract: Graph neural networks (GNNs) have achieved remarkable success in learning graph representations, especially graph Transformers, which have recently shown superior performance on various ...
Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States Amphionic Inc, Ann Arbor, Michigan 48109, United States Department of Chemical Engineering, ...
ABSTRACT: Stable distributions are well-known for their desirable properties and can effectively fit data with heavy tail. However, due to the lack of an explicit probability density function and ...
Monograph's in-depth journey delves into the soul, revealing the essence of a subject with precision and passion. Monograph's in-depth journey delves into the soul, revealing the essence of a subject ...
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