2 citations · 2 across the 2 of their papers we have counts for
3 papers
Measuring Learned Equivariance: An Information Theoretic Approach
Claire Schlesinger, Wolfgang Gatterbauer, Robin Walters
Learning symmetries through data augmentation offers an alternative to architectural equivariance, but does not guarantee that a network respects those symmetries. Measuring learne…
FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties
Circe Hsu, Claire Schlesinger, Karan Mudaliar +3
The work function and cleavage energy of a surface are critical properties that determine the viability of materials in electronic emission applications, semiconductor devices, and…
MatrixNet: Learning over symmetry groups using learned group representations
Lucas Laird, Circe Hsu, Asilata Bapat +1
Group theory has been used in machine learning to provide a theoretically grounded approach for incorporating known symmetry transformations in tasks from robotics to protein model…