Fetching the paper…
Reading the bibliography…
Density estimation is a fundamental problem in statistical learning.
Spatial interaction and the statistical analysis of lattice systems
Besag, Julian · 1974
Earlier work this paper cites.
Learning and relearning in Boltzmann machines
Hinton, Geoffrey E and Sejnowski, Terrence J · 1986
Earlier work this paper cites.
Independent component filters of natural images compared with simple cells in primary visual cortex
Van Hateren, J Hans and van der Schaaf, Arjen · 1998
Earlier work this paper cites.
Products of experts
Hinton, Geoffrey E · 1999
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, Aapo · 2005
Earlier work this paper cites.
Unsupervised discovery of nonlinear structure using contrastive backpropagation
Hinton, Geoffrey, Osindero, Simon, Welling, Max, and Teh, Yee-Whye · 2006
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, Geoffrey E · 2006
Earlier work this paper cites.
Connections between score matching, contrastive divergence, and pseudolikelihood for continuous-valued variables
Hyvärinen, Aapo · 2007
Earlier work this paper cites.
Optimal approximation of signal priors
Hyvärinen, Aapo · 2008
Earlier work this paper cites.
Learning deep architectures for AI
Bengio, Yoshua · 2009
Earlier work this paper cites.
Interpretation and generalization of score matching
Lyu, Siwei · 2009
Cited alongside, same era.
Regularized estimation of image statistics by score matching
Kingma, Diederik P and LeCun, Yann · 2010
Cited alongside, same era.
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Girolami, Mark and Calderhead, Ben · 2011
Cited alongside, same era.
Least squares estimation without priors or supervision
Raphan, Martin and Simoncelli, Eero P · 2011
Cited alongside, same era.
New method for parameter estimation in probabilistic models: minimum probability flow
Sohl-Dickstein, Jascha, Battaglino, Peter B, and DeWeese, Michael R · 2011
Cited alongside, same era.
A connection between score matching and denoising autoencoders
Vincent, Pascal · 2011
Auto-encoding variational Bayes
Kingma, Diederik P and Welling, Max · 2013
Later among the works it cites.
What regularized auto-encoders learn from the data-generating distribution
Alain, Guillaume and Bengio, Yoshua · 2014
Later among the works it cites.
Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Later among the works it cites.
Adam: A method for stochastic optimization
Kingma, Diederik P and Ba, Jimmy · 2014
Later among the works it cites.
A note on the evaluation of generative models
Theis, Lucas, Oord, Aäron van den, and Bethge, Matthias · 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…
Cited alongside, same era.
Digital image processing
Gonzalez, Rafael C and Woods, Richard E · 2012
Cited alongside, same era.
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Gutmann, Michael U and Hyvärinen, Aapo · 2012
Cited alongside, same era.
Representation learning: A review and new perspectives
Bengio, Yoshua, Courville, Aaron, and Vincent, Pascal · 2013
Cited alongside, same era.
Estimating the hessian by back-propagating curvature
Martens, James, Sutskever, Ilya, and Swersky, Kevin
Cited in the paper.
Estimating the Hessian by back-propagating curvature
Martens, James, Sutskever, Ilya, and Swersky, Kevin
Cited in the paper.
Tensorflow: A system for large-scale machine learning
Abadi, Martín, Barham, Paul, Chen, Jianmin, Chen, Zhifeng, Davis, Andy, Dean, Jeffrey, Devin, Matthieu, Ghemawat, Sanjay, Irving, Geoffrey, Isard, Michael, et al · 2016
Later among the works it cites.
Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Hyvärinen, Aapo and Morioka, Hiroshi · 2016
Later among the works it cites.
Generalizing Hamiltonian Monte Carlo with neural networks
Levy, Daniel, Hoffman, Matthew D, and Sohl-Dickstein, Jascha · 2017
Later among the works it cites.
Learning deep energy models: contrastive divergence vs. amortized MLE
Liu, Qiang and Wang, Dilin · 2017
Later among the works it cites.