Resources

• Uncertainty Decomposition in BNNs with Latent Variables - arxiv

• Probabilistic Numerics and Uncertainty in Computations - Paper

• Bayesian Inference of Log Determinants - Paper

Discussions

• Variational Bayesian Inference vs Monte-Carlo Dropout for Uncertainty Quantification in DL - reddit

So there are a few Benchmark datasets we can look at to determine

Current top:

• MC Dropout
• Mean-Field Variational Inference
• Deep Ensembles
• Ensemble MC Dropout

Benchmark Repos:

• OATML
• Hugh Salimbeni

Resources

• Neural Network Diagrams - stack
• MLSS 2019, Moscow - Yarin Gal - Prezi I | Prezi II
• Fast and Scalable Estimation of Uncertainty using Bayesian Deep Learning - Blog
• Making Your Neural Network Say "I Don't Know" - Bayesian NNs using Pyro and PyTorch - Blog
• How Bayesian Methods Embody Occam's razor - blog
• DropOUt as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning - blog
• Uncertainty Estimation in Supervised Learning - Video | Slides

Blogs

• Regression with Probabilistic Layers in TensorFlow Probability
• Variational Inference for Bayesian Neural Networks (2019) | TensorFlow
• Brenden Hasz
  • Bayesian Regressions with MCMC or Variational Bayes using TensorFlow Probability
  • Bayesian Gaussian Mixture Modeling with Stochastic Variational Inference
  • Trip Duration Prediction using Bayesian Neural Networks and TensorFlow 2.0
• Yarin Gal
  • What My Deep Model Doesn't Know...
• High Level Series of Posts
  • Probabilistic Deep Learning: Bayes by Backprop
  • When machine learning meets complexity: why Bayesian deep learning is unavoidable
  • Bayesian Convolutional Neural Networks with Bayes by Backprop
  • Reflections on Bayesian Inference in Probabilistic Deep Learning

Software

• TensorFlow Probability
  • Edward2
• PyTorch
  • Pyro

Papers

• DropOut as Bayesian Approximation - Paper | Code | Tutorial
• Uncertainty Decomposition in BNNs with Latent Variables - arxiv
• Practical Deep Learning with Bayesian Principles - arxiv
• Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks - Foong et. al. (2019) - Paper
• Probabilistic Numerics and Uncertainty in Computations - Paper
• Bayesian Inference of Log Determinants - Paper

Code

• A Regression Master Class with Aboleth
• BNN Implementations - Github
• A Comprehensive Guide to Bayesian CNN with Variational Inference - Shridhar et al. (2019) - Github