[DeepBayes2018]: Day 3, Practical session 3. Reinforcement learning
Speaker: Alexander Fritsler
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4 years ago
01:41:54
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[DeepBayes2018]: Day 3, Invited talk 1. Advanced methods of variational inference
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[DeepBayes2018]: Day 1, practical session 2. Bayesian reasoning
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01:31:59
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[DeepBayes2018]: Day 1, lecture 3. Models with latent variables and EM-algorithm
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00:43:32
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[DeepBayes2018]: Day 1, practical session 4. EM-algorithm (part 1)
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00:07:32
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[DeepBayes2018]: Day 1, practical session 5. EM-algorithm (part 2)
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[DeepBayes2018]: Day 2, lecture 1. Introduction to stochastic optimization
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[DeepBayes2018]: Day 2, lecture 2. Scalable Bayesian methods
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[DeepBayes2018]: Day 2, lecture 3. Variational autoencoders
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[DeepBayes2018]: Day 2, lecture 4. Discrete latent variables
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[DeepBayes2018]: Day 2, practical session 5. Variational autoencoders
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01:05:49
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[DeepBayes2018]: Day 3, IT 2. Reinforcement learning through the lense of variational inference
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00:59:27
2
[DeepBayes2018]: Day 3, Practical session 3. Reinforcement learning
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01:02:19
2
[DeepBayes2018]: Day 3, lecture 4. Distributional reinforcement learning
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00:27:38
5
[DeepBayes2018]: Day 3, Practical session 5. Distributional reinforcement learning
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[DeepBayes2018]: Day 4, lecture 1. Generative models
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00:12:44
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[DeepBayes2018]: Day 4, Practical session 2. Adversarial learning
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[DeepBayes2018]: Day 4, Invited talk 3. Extending the Reparameterization Trick
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[DeepBayes2018]: Day 4, Sponsor talk 4. Yaroslav LabutinRymsho. Samsung Research
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[DeepBayes2018]: Day 5, Lecture 1. Gaussian processes
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[DeepBayes2018]: Day 5, Practical session 2. Bayesian optimization
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[DeepBayes2018]: Day 5, Invited talk 3. Deep Gaussian processes
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[DeepBayes2018]: Day 5, lecture 4. Markov chain Monte Carlo
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3
[DeepBayes2018]: Day 5, Lecture 5. Stochastic Markov chain Monte Carlo
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[DeepBayes2018]: Day 6, Lecture 1. Bayesian neural networks and variational dropout
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