Deep Learning: Chapter 20
20 Deep Generative Models
20.1 Boltzmann Machines
20.2 Restricted Boltzmann Machines
20.2.1 Conditional Distributions
20.2.2 Training Restricted Boltzmann Machines
20.3 Deep Belief Networks
20.4 Deep Boltzmann Machines
20.4.1 Interesting Properties
20.4.2 DBM Mean Field Inference
20.4.3 DBM Parameter Learning
20.4.4 Layer-Wise Pretraining
20.4.5 Jointly Training Deep Boltzmann Machines
centered deep Boltzmann machine
multi-prediction deep Boltzmann machine
20.5 Boltzmann Machines for Real-Valueed Data
20.5.1 Gaussian-Bernoulli RBMs
20.5.2 Undirected Models of Conditional Covariance
Mean and Covariance RBM
Mean Product of Student t-distributions ???
Spike and Slab RBM ???
20.6 Convolutional Boltzmann Machines
20.7 Boltzmann Machines for Structured or Sequential Outputs
20.8 Other Boltzmann Machines
20.9 Back-Propagation throughRandom Operations
20.9.1 Back-Propagating through Discrete Stochastic Operations
20.10 Directed Generative Nets
20.10.1 Sigmoid Belief Nets
20.10.2 Differentiable Generator Nets
inverse transform sampling ???
20.10.3 Variational Autoencoders
http://fiveeyes.github.io/learning/2017/08/25/Variational-Inference.html
https://github.com/FiveEyes/ml-notebook/blob/master/vae/vae.ipynb
deep recurrent attention write(DRAW)
20.10.4 Generative Adversarial Networks
DCGAN
conditional GAN