DeepBayes2018: Day 6, Practical session 3. Neural networks sparsification

DeepBayes2018: Day 3, Invited talk 1. Advanced methods of variational inference

DeepBayes2018: Day 1, practical session 2. Bayesian reasoning

DeepBayes2018: Day 1, lecture 3. Models with latent variables and EM-algorithm

DeepBayes2018: Day 1, practical session 4. EM-algorithm (part 1)

DeepBayes2018: Day 1, practical session 5. EM-algorithm (part 2)

DeepBayes2018: Day 2, lecture 1. Introduction to stochastic optimization

DeepBayes2018: Day 2, lecture 2. Scalable Bayesian methods

DeepBayes2018: Day 2, lecture 3. Variational autoencoders

DeepBayes2018: Day 2, lecture 4. Discrete latent variables

DeepBayes2018: Day 2, practical session 5. Variational autoencoders

DeepBayes2018: Day 3, IT 2. Reinforcement learning through the lense of variational inference

DeepBayes2018: Day 3, Practical session 3. Reinforcement learning

DeepBayes2018: Day 3, lecture 4. Distributional reinforcement learning

DeepBayes2018: Day 3, Practical session 5. Distributional reinforcement learning

DeepBayes2018: Day 4, lecture 1. Generative models

DeepBayes2018: Day 4, Practical session 2. Adversarial learning

DeepBayes2018: Day 4, Invited talk 3. Extending the Reparameterization Trick

DeepBayes2018: Day 4, Sponsor talk 4. Yaroslav LabutinRymsho. Samsung Research

DeepBayes2018: Day 5, Lecture 1. Gaussian processes

DeepBayes2018: Day 5, Practical session 2. Bayesian optimization

DeepBayes2018: Day 5, Invited talk 3. Deep Gaussian processes

DeepBayes2018: Day 5, lecture 4. Markov chain Monte Carlo

DeepBayes2018: Day 5, Lecture 5. Stochastic Markov chain Monte Carlo

DeepBayes2018: Day 6, Lecture 1. Bayesian neural networks and variational dropout