Fetching the paper…
Reading the bibliography…
We revisit the challenging problem of training Gaussian-Bernoulli restricted Boltzmann machines (GRBMs), introducing two innovations.
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
Stuart Geman and Donald Geman · 1984
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski · 1985
Earlier work this paper cites.
Information processing in dynamical systems: foundations of harmony theory
P Smolensky · 1986
Earlier work this paper cites.
Unsupervised learning of distributions on binary vectors using two layer networks
Yoav Freund and David Haussler · 1991
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.
Probabilistic inference using Markov chain Monte Carlo methods
Radford M Neal · 1993
Earlier work this paper cites.
Representations of knowledge in complex systems
Ulf Grenander and Michael I Miller · 1994
Earlier work this paper cites.
Exponential convergence of langevin distributions and their discrete approximations
Gareth O Roberts and Richard L Tweedie · 1996
Earlier work this paper cites.
An introduction to variational methods for graphical models
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
Earlier work this paper cites.
An introduction to mcmc for machine learning
Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I Jordan · 2003
Earlier work this paper cites.
Exponential family harmoniums with an application to information retrieval
Max Welling, Michal Rosen-Zvi, and Geoffrey E Hinton · 2004
Earlier work this paper cites.
Parallel tempering: Theory, applications, and new perspectives
David J Earl and Michael W Deem · 2005
Earlier work this paper cites.
Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
Cited alongside, same era.
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh · 2006
Cited alongside, same era.
Sparse deep belief net model for visual area v2
Honglak Lee, Chaitanya Ekanadham, and Andrew Ng · 2007
Cited alongside, same era.
Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Cited alongside, same era.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Cited alongside, same era.
Training restricted boltzmann machine via the thouless-anderson-palmer free energy
Marylou Gabrié, Eric W Tramel, and Florent Krzakala · 2015
Later among the works it cites.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Later among the works it cites.
Mean-field inference in gaussian restricted boltzmann machine
Chako Takahashi and Muneki Yasuda · 2016
Later among the works it cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Later among the works it cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Later among the works it cites.
Gaussian-binary restricted boltzmann machines for modeling natural image statistics
Jan Melchior, Nan Wang, and Laurenz Wiskott · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Modeling pixel means and covariances using factorized third-order boltzmann machines
Marc’Aurelio Ranzato and Geoffrey E Hinton · 2010
Cited alongside, same era.
Factored 3-way restricted boltzmann machines for modeling natural images
Marc’Aurelio Ranzato, Alex Krizhevsky, and Geoffrey Hinton · 2010
Cited alongside, same era.
In all likelihood, deep belief is not enough
Lucas Theis, Sebastian Gerwinn, Fabian Sinz, and Matthias Bethge · 2011
Cited alongside, same era.
Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
Cited alongside, same era.
An analysis of gaussian-binary restricted boltzmann machines for natural images
Nan Wang, Jan Melchior, and Laurenz Wiskott · 2012
Cited alongside, same era.
Gaussian-bernoulli deep boltzmann machine
Kyung Hyun Cho, Tapani Raiko, and Alexander Ilin · 2013
Cited alongside, same era.
Later among the works it cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Later among the works it cites.
Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
Later among the works it cites.
High-dimensional bayesian inference via the unadjusted langevin algorithm
Alain Durmus and Eric Moulines · 2019
Later among the works it cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Later among the works it cites.
Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
Later among the works it cites.
Learning gaussian-bernoulli rbms using difference of convex functions optimization
Vidyadhar Upadhya and PS Sastry · 2021
Later among the works it cites.
Approximation properties of gaussian-binary restricted boltzmann machines and gaussian-binary deep belief networks
Linyan Gu, Lihua Yang, and Feng Zhou · 2022
Closest in time.