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Deep Gaussian processes
Published on Oct 29, 20144622 Views
In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inp
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Chapter list
Deep Gaussian Processes00:00
Outline - 100:16
Deep Neural Network - 100:19
Deep Neural Network - 200:49
Mathematically - 102:01
Overfitting02:07
Deep Neural Network - 303:55
Mathematically - 104:36
A Cascade of Neural Networks04:42
Replace Each Neural Network with a Gaussian Process04:52
Untitled05:50
Gaussian Processes: Extremely Short Overview08:28
Outline - 210:22
Mathematically10:26
Why Deep?11:37
Difficulty for Probabilistic Approaches - 113:03
Untitled13:30
Difficulty for Probabilistic Approaches - 213:59
Analysis of Deep GPs14:20
Variational Compression - 114:23
Variational Compression - 214:51
Structures for Extracting Information from Data18:21
Damianou and Lawrence (2013)18:38
Collapsed Deep GPs18:50
Derivative Tails Increase with Layers: Step Function19:19
Loop Detection in Robotics24:41
Data fit for Loop Closure25:59
Motion Capture26:26
Deep hierarchies – motion capture26:33
Digits Data Set27:34
Deep hierarchies – MNIST27:37
Deep Health29:12
Summary30:05
References30:49