2 papers
cs.LG2026
How Do Transformers Learn to Represent Symmetries?
Eduardo Santos-Escriche, Valerie Engelmayer, Ya-Wei Eileen Lin +1
Training Transformer-based architectures with finite data augmentation has become an increasingly popular approach in geometric machine learning. Despite its empirical success, the…
cs.LG2025
LieAugmenter: Equivariant Learning by Discovering Symmetries with Learnable Augmentations
Eduardo Santos-Escriche, Ya-Wei Eileen Lin, Stefanie Jegelka
Data augmentation is a powerful mechanism in equivariant machine learning, encouraging symmetry by training networks to produce consistent outputs under transformed inputs. Yet, ef…