sjchoi86 / Bayes Nn
Lecture notes on Bayesian deep learning
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Understanding Bayesian Deep Learning
1. Elementary mathematics
- Set theory
- Measure theory
- Probability
- Random variable
- Random process
- Functional analysis (harmonic analysis)
2. Gaussian process
- Gaussian process
- Weight-space view
- Function-space view
- Gaussian process latent variable model
3. Bayesian neural netwrok
- Minimum description length
- Ensemble learning in Bayesian neural network
- Practical variational inference
- Bayes by backprop
- Summary of variational inference
- Dropout as a Bayesian approximation
- Stein variational gradient descent
4. Summary
- Measure thoery
- Probability
- Random variable
- Random process
- Gaussian process
- Functional Analysis
- Summary of variational inference
- Stein variational gradient descent
5. Uncertainty in Deep Learning
- Yarin Gal, Uncertainty in Deep Learning
- Anonymous, Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
- Patrick McClure, Representing Inferential Uncertainty in Deep Neural Networks through Sampling
- Balaji Lakshminarayanan, Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
- Alex Kendal, What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
- Gregory Kahn, Uncertainty-Aware Reinforcement Learning for Collision Avoidance
- Charles Richter, Safe Visual Navigation via Deep Learning and Novelty Detection
- Sungjoon Choi, Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling
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