Fetching the paper…
Reading the bibliography…
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.
Sample-based non-uniform random variate generation
Luc Devroye · 1986
Earlier work this paper cites.
Information processing in dynamical systems: Foundations of harmony theory
Paul Smolensky · 1986
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1988
Earlier work this paper cites.
Artificial neural networks and their application to sequence recognition
Yoshua Bengio · 1991
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
An information-maximization approach to blind separation and blind deconvolution
Anthony J Bell and Terrence J Sejnowski · 1995
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.
Higher order statistical decorrelation without information loss
Gustavo Deco and Wilfried Brauer · 1995
Earlier work this paper cites.
Mean field theory for sigmoid belief networks
Lawrence K Saul, Tommi Jaakkola, and Michael I Jordan · 1996
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Graphical models for machine learning and digital communication
Brendan J Frey · 1998
Earlier work this paper cites.
A view of the em algorithm that justifies incremental, sparse, and other variants
Radford M Neal and Geoffrey E Hinton · 1998
Earlier work this paper cites.
Modeling high-dimensional discrete data with multi-layer neural networks
Yoshua Bengio and Samy Bengio · 1999
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Aapo Hyvärinen and Petteri Pajunen · 1999
Earlier work this paper cites.
Gaussianization
Scott Shaobing Chen and Ramesh A Gopinath · 2000
Earlier work this paper cites.
Independent component analysis
Aapo Hyvärinen, Juha Karhunen, and Erkki Oja · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey E Hinton · 2009
Earlier work this paper cites.
The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
Earlier work this paper cites.
Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
Earlier work this paper cites.
Mathias Berglund and Tapani Raiko · 2013
Earlier work this paper cites.
Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
High-dimensional probability estimation with deep density models
Oren Rippel and Ryan Prescott Adams · 2013
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
Cited alongside, same era.
Nice: non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Cited alongside, same era.
Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2014
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Later among the works it cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Later among the works it cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Later among the works it cites.
Generative image modeling using spatial lstms
Lucas Theis and Matthias Bethge · 2015
Later among the works it cites.
A note on the evaluation of generative models
Lucas Theis, Aäron Van Den Oord, and Matthias Bethge · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Andriy Mnih and Karol Gregor · 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.
Markov chain monte carlo and variational inference: Bridging the gap
Tim Salimans, Diederik P Kingma, and Max Welling · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Understanding symmetries in deep networks
Vijay Badrinarayanan, Bamdev Mishra, and Roberto Cipolla · 2015
Cited alongside, same era.
Density modeling of images using a generalized normalization transformation
Johannes Ballé, Valero Laparra, and Eero P Simoncelli · 2015
Cited alongside, same era.
Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 2015
Cited alongside, same era.
Dustin Tran, Rajesh Ranganath, and David M Blei · 2015
Later among the works it cites.
Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur · 2015
Later among the works it cites.
Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
Later among the works it cites.
Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
Later among the works it cites.
Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
Later among the works it cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
Closest in time.
Towards conceptual compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, and Daan Wierstra · 2016
Closest in time.
Continuous deep q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
Closest in time.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Closest in time.
Generating images with recurrent adversarial networks
Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic · 2016
Closest in time.
Exploring the limits of language modeling
Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
Closest in time.
Improving variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, and Max Welling · 2016
Closest in time.
Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
Closest in time.
Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Closest in time.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Diederik P Kingma · 2016
Closest in time.
Resnet in resnet: Generalizing residual architectures
Sasha Targ, Diogo Almeida, and Kevin Lyman · 2016
Closest in time.
Learning functions across many orders of magnitudes
Hado van Hasselt, Arthur Guez, Matteo Hessel, and David Silver · 2016
Closest in time.
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
Closest in time.