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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
Open Source Agenda is not affiliated with "Bayes Nn" Project. README Source:
sjchoi86/bayes-nn
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6 years ago
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sjchoi86/bayes-nn
Tags
Deep Learning
Probability Theory
Variational Inference
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