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cs.LG2025
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
Improving Equivariant Networks with Probabilistic Symmetry Breaking
Hannah Lawrence, Vasco Portilheiro, Yan Zhang +1
Equivariance encodes known symmetries into neural networks, often enhancing generalization. However, equivariant networks cannot break symmetries: the output of an equivariant netw…
cs.LG2019
Representation Learning with Multisets
Vasco Portilheiro
We study the problem of learning permutation invariant representations that can capture "flexible" notions of containment. We formalize this problem via a measure theoretic definit…