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Learning Deep Generative Models
Published on Aug 23, 201614465 Views
In this tutorial I will discuss mathematical basics of many popular deep generative models, including Restricted Boltzmann Machines (RBMs), Deep Boltzmann Machines (DBMs), Helmholtz Machines, Variatio
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Chapter list
Learning Deep Generative Models 00:00
Mining for Structure/100:02
Mining for Structure/200:46
Example: Understanding Images01:01
Talk Roadmap02:33
Restricted Boltzmann Machines03:38
Learning Features05:15
Model Learning/1 05:44
Model Learning/207:42
RBMs for Real-valued Data/108:22
RBMs for Real-valued Data/209:04
RBMs for Real-valued Data/309:10
RBMs for Word Counts/109:26
RBMs for Word Counts/210:00
Different Data Modalities 10:36
Product of Experts/111:24
Product of Experts12:52
Deep Boltzmann Machines/112:58
Deep Boltzmann Machines/213:09
Model Formulation13:48
Mathematical Formulation15:00
Approximate Learning/115:48
Approximate Learning/216:04
Approximate Learning17:23
Stochastic Approximation18:05
Learning Algorithm19:10
Variational Inference/121:26
Variational Inference/226:09
Variational Inference/327:13
Variational Inference/428:47
Good Generative Model?/1 28:49
Good Generative Model?/229:15
Good Generative Model?/3 29:22
Good Generative Model?/4 29:30
Good Generative Model?/529:36
Good Generative Model?/630:11
Handwriting Recognition30:40
Generative Model of 3-D Objects30:47
3-D Object Recognition 31:18
Learning Hierarchical Representations 31:38
Talk Roadmap31:51
Helmholtz Machines31:54
Various Deep Generative Models34:12
Motivating Example 35:04
Overall Model 35:55
Flipping Colors 36:34
Flipping Backgrounds 37:06
Flipping Objects 37:31
Qualitative Comparison 38:00
Variational Lower-Bound 38:15
Novel Scene Compositions38:45
Overall Model40:21
Variational Autoencoders (VAEs)41:18
VAE: Example 42:43
Recognition Network43:16
Variational Bound 44:37
Reparameterization Trick/146:43
Reparameterization Trick/247:48
Computing the Gradients/148:49
Computing the Gradients/250:27
VAE: Assumptions51:46
Importance Weighted Autoencoders/152:25
Importance Weighted Autoencoders/254:10
Tighter Lower Bound54:51
Computing the Gradients 56:12
IWAEs vs. VAEs/156:42
IWAE: Intuition/157:53
IWAE: Intuition/258:46
Computation with IWAEs59:27
Two Architectures 01:00:08
MNIST Results/1 01:01:14
IWAEs vs. VAEs/201:01:23
IWAEs vs. VAEs/301:01:27
MNIST Results/2 01:02:02
Key Observation01:02:39
Talk Roadmap01:05:56
Caption Generation/1 01:06:17
Encode-Decode Framework01:06:36
Caption Generation/2 01:06:48
Caption Generation/3 01:07:10
Caption Generation with Visual Attention01:07:31
Visual Attention01:07:57
Improving Action Recognition01:08:18
Recurrent Attention Model/1 01:08:29
Recurrent Attention Model/2 01:09:46
Model Definition01:10:25
Variational Learning/101:11:16
Variational Learning/201:11:59
Variational Learning/301:12:52
Sampling from the Prior01:13:09
Key Observation01:13:18
Maximizing Marginal Likelihood01:14:20
Comparing the Two Estimators01:14:56
Another Key Observation/101:15:51
Another Key Observation/201:16:29
Relationship To Helmholtz Machines/101:16:39
Relationship To Helmholtz Machines/2 01:16:59
The Wake-Sleep Recurrent Attention Model 01:18:18
Training Inference Network01:18:33
MNIST Attention Demo01:18:58
Hard vs. Sod Attention 01:19:25
Talk Roadmap 01:20:07
(Some) Open Problems/1 01:20:17
(Some) Open Problems/2 01:20:28
Sequence to Sequence Learning 01:21:14
Skip-Thought Model/1 01:21:26
Skip-Thought Model/2 01:21:40
Learning Objective01:21:52
Semantic Relatedness01:22:06
Semantic Relatedness Recurrent Neural Network 01:22:31
Neural Story Telling 01:24:51
Hierarchical RNNs/101:25:37
Hierarchical RNNs/201:25:53
Atari Games01:26:20
Actor-Mimic Net in Action01:26:39
Transfer Learning01:26:42
Summary01:26:46
Thank You01:27:35