Tutorial on Variational Autoencoders

Sample-based non-uniform random variate generation

Luc Devroye · 1986

Earlier work this paper cites.

Keeping the neural networks simple by minimizing the description length of the weights

Geoffrey E Hinton and Drew Van Camp · 1993

Earlier work this paper cites.

Autoencoders, minimum description length, and helmholtz free energy

Geoffrey E Hinton and Richard S Zemel · 1994

Earlier work this paper cites.

The helmholtz machine

Peter Dayan, Geoffrey E Hinton, Radford M Neal, and Richard S Zemel · 1995

Earlier work this paper cites.

The" wake-sleep" algorithm for unsupervised neural networks

Geoffrey E Hinton, Peter Dayan, Brendan J Frey, and Radford M Neal · 1995

Earlier work this paper cites.

Emergence of simple-cell receptive field properties by learning a sparse code for natural images

Bruno A Olshausen and David J Field · 1996

Earlier work this paper cites.

Efficient sparse coding algorithms

Honglak Lee, Alexis Battle, Rajat Raina, and Andrew Y Ng · 2006

Earlier work this paper cites.

A fast learning algorithm for deep belief nets

Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006

Earlier work this paper cites.

Extracting and composing robust features with denoising autoencoders

Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008

Cited alongside, same era.

Deep boltzmann machines

Ruslan Salakhutdinov and Geoffrey E Hinton · 2009

Cited alongside, same era.

Imagenet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton · 2012

Cited alongside, same era.

Generalized denoising auto-encoders as generative models

Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013

Cited alongside, same era.

Auto-encoding variational Bayes

Diederik P Kingma and Max Welling · 2014

Cited alongside, same era.

Stochastic backpropagation and approximate inference in deep generative models

Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014

Cited alongside, same era.

Caffe: Convolutional architecture for fast feature embedding

Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014

Later among the works it cites.

Markov chain monte carlo and variational inference: Bridging the gap

Tim Salimans, Diederik Kingma, and Max Welling · 2015

Later among the works it cites.

Deep convolutional inverse graphics network

Tejas D Kulkarni, William F. Whitney, Pushmeet Kohli, and Josh Tenenbaum · 2015

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Draw: A recurrent neural network for image generation

Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015

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Learning structured output representation using deep conditional generative models

Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015

Later among the works it cites.

Adam: A method for stochastic optimization

Diederik Kingma and Jimmy Ba · 2015