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Papers

This page highlights some of the key papers that we will go over during the working group. I have organized the sections into topics and each section will have the appropriate literature. I will keep the papers that we will go in detail near the top and any supporting papers will go at the bottom.

!> Warning: This is not the final list. This is merely a guide that we will pivot off of.

Bayesian

  • The prior can generally only be understood in the context of the likelihood - Gelman et. al. (2017) - arxiv | blog

Introduction to Bayesian Deep Learning

  • Practical Deep Learning with Bayesian Principles - Warner & Neal (1997) -arxiv
  • Towards Bayesian Deep Learning: A Survey - Wang et. al. (2016)

Approximately Bayesian

  • Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning - Gal & Ghahramani - Paper | Code | Tutorial | Blog

Computer Vision

  • What Uncertainties do We Need for BDL for CV - Kendall & Gal (2017) - arxiv |
  • A Comprehensive guide to Bayesian Convolutional NeuralNetwork with Variational Inference - Shridhar et. al. (2018) - arxiv | code (PyTorch)

SOTA

Bayesian Deep Learning and a Probabilistic Perspective of Generalization - AGW & Pavel Izmailov - PDF

The Case for Bayesian Deep Learning - AGW - ARXIV

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning - Zhang et al - arxiv | Code