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A Bayesian Approach to Invariant Deep Neural Networks

arXiv 2107.09301 ↗First posted 20 Jul 2021stat.ML cs.LGcited by 1

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Abstract (arXiv)

We propose a novel Bayesian neural network architecture that can learn invariances from data alone by inferring a posterior distribution over different weight-sharing schemes. We show that our model outperforms other non-invariant architectures, when trained on datasets that contain specific invariances. The same holds true when no data augmentation is performed.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 20 Jul 2021.

Builds on

22 of 33 references.

References · 22

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  2. 2016Group Equivariant Convolutional Networkscited by 1,165
  3. 2015Weight Uncertainty in Neural Networkscited by 554
  4. 2015Variational Dropout and the Local Reparameterization Trickcited by 490
  5. 2021What Are Bayesian Neural Network Posteriors Really Like?cited by 308
  6. 2018On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groupscited by 300
  7. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  8. 2020MDP Homomorphic Networks: Group Symmetries in Reinforcement Learningcited by 142
  9. 2017Equivariance Through Parameter-Sharingcited by 141
  10. 2018Universal approximations of invariant maps by neural networkscited by 127
  11. 2021Bayesian Neural Network Priors Revisitedcited by 121
  12. 2021Scalable Marginal Likelihood Estimation for Model Selection in Deep Learningcited by 106
10 more
  1. 2021Repulsive Deep Ensembles are Bayesiancited by 104
  2. 2021Priors in Bayesian Deep Learning: A Reviewcited by 101
  3. 2020On the Benefits of Invariance in Neural Networkscited by 79
  4. 2020Meta-Learning Symmetries by Reparameterizationcited by 76
  5. 2020Learning Invariances in Neural Networkscited by 52
  6. 2018Learning Invariances using the Marginal Likelihoodcited by 51
  7. 2019A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inferencecited by 51
  8. 2021On Stein Variational Neural Network Ensemblescited by 21
  9. 2021BNNpriors: A library for Bayesian neural network inference with different prior distributionscited by 20
  10. 2021Neural Networks for Learning Counterfactual G-Invariances from Single Environmentscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Equivariance and Augmentation for Bayesian Neural Networkscited by 0