4 papers · 1 filter
Equivariance and Augmentation for Bayesian Neural Networks
Miaowen Dong, Axel Flinth, Jan E. Gerken
Symmetries are important for many deep learning tasks, ranging from applications in the sciences to medical imaging. However, there is an ongoing debate about whether to impose sym…
Conservation Laws from Data Symmetry in Neural Networks
Jakob Galley, Vahid Shahverdi, Axel Flinth
We explore whether intrinsic symmetries of the training data lead to conserved quantities during gradient-flow training of neural networks. Under the assumption that the loss funct…
Ensembles provably learn equivariance through data augmentation
Oskar Nordenfors, Axel Flinth
Recently, it was proved that group equivariance emerges in ensembles of neural networks as the result of full augmentation in the limit of infinitely wide neural networks (neural t…
Optimization Dynamics of Equivariant and Augmented Neural Networks
Oskar Nordenfors, Fredrik Ohlsson, Axel Flinth
We investigate the optimization of neural networks on symmetric data, and compare the strategy of constraining the architecture to be equivariant to that of using data augmentation…