Authors that are also TMLR Expert Reviewers: ~Vincent_Fortuin1
Abstract: Conventional Bayesian Neural Networks (BNNs) are unable to leverage unlabelled data to improve their predictions. To overcome this limitation, we introduce Self-Supervised Bayesian Neural Networks, which use unlabelled data to learn models with suitable prior predictive distributions. This is achieved by leveraging contrastive pretraining techniques and optimising a variational lower bound. We then show that the prior predictive distributions of self-supervised BNNs capture problem semantics better than conventional BNN priors. In turn, our approach offers improved predictive performance over conventional BNNs, especially in low-budget regimes.
Certifications: Expert Certification
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Manuel_Haussmann1
Submission Number: 2167
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