Priors in Bayesian Deep Learning: A Review

Author:

Fortuin Vincent1ORCID

Affiliation:

1. Department of Computer Science ETH Zürich Zürich Switzerland

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference265 articles.

1. Aneja J. Schwing A. Kautz J.&Vahdat A.(2020).NCP‐VAE: Variational autoencoders with noise contrastive priors. arXiv preprint arXiv:2010.02917.

2. Arora S. Du S.S. Li Z. Salakhutdinov R. Wang R.&Yu D.(2019).Harnessing the power of infinitely wide deep nets on small‐data tasks. InInternational Conference on Learning Representations.

3. Ashman M. So J. Tebbutt W. Fortuin V. Pearce M.&Turner R.E.(2020).Sparse Gaussian process variational autoencoders. arXiv preprint arXiv:2010.10177.

4. Atanov A. Ashukha A. Struminsky K. Vetrov D.&Welling M.(2018).The deep weight prior. InInternational Conference on Learning Representations.

5. Bauer M.&Mnih A.(2019).Resampled priors for variational autoencoders. InThe 22nd International Conference on Artificial Intelligence and Statistics pp.66–75. PMLR.

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