3 papers
cs.LG2026
To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking
Hannah Lawrence, Elyssa Hofgard, Vasco Portilheiro +3
Symmetry-aware methods for machine learning, such as data augmentation and equivariant architectures, encourage correct model behavior on all transformations (e.g. rotations or per…
cs.LG2025
Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution
Rui Wang, Elyssa Hofgard, Han Gao +2
Modeling symmetry breaking is essential for understanding the fundamental changes in the behaviors and properties of physical systems, from microscopic particle interactions to mac…
cs.LG2024
Relaxed Equivariant Graph Neural Networks
Elyssa Hofgard, Rui Wang, Robin Walters +1
3D Euclidean symmetry equivariant neural networks have demonstrated notable success in modeling complex physical systems. We introduce a framework for relaxed graph equivari…