Emergent Equivariance in Deep Ensembles
Abstract
We show that deep ensembles become equivariant for all inputs and at all training times by simply using data augmentation. Crucially, equivariance holds off-manifold and for any architecture in the infinite width limit. The equivariance is emergent in the sense that predictions of individual ensemble members are not equivariant but their collective prediction is. Neural tangent kernel theory is used to derive this result and we verify our theoretical insights using detailed numerical experiments.
Cite
Text
Gerken and Kessel. "Emergent Equivariance in Deep Ensembles." International Conference on Machine Learning, 2024.Markdown
[Gerken and Kessel. "Emergent Equivariance in Deep Ensembles." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/gerken2024icml-emergent/)BibTeX
@inproceedings{gerken2024icml-emergent,
title = {{Emergent Equivariance in Deep Ensembles}},
author = {Gerken, Jan E and Kessel, Pan},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {15438-15465},
volume = {235},
url = {https://mlanthology.org/icml/2024/gerken2024icml-emergent/}
}