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Boltzmann machines are powerful distributions that have been shown to be an effective prior over binary latent variables in variational autoencoders (VAEs).
Calculation of partition functions
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Zakkula Govindarajulu · 1966
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Likelihood ratio gradient estimation for stochastic systems
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Introduction to the theory of neural computation
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Annealed importance sampling
Radford M. Neal · 2001
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A new learning algorithm for mean field Boltzmann machines
Max Welling and Geoffrey E Hinton · 2002
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Population annealing and its application to a spin glass
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Reducing the dimensionality of data with neural networks
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Restricted Boltzmann machines for collaborative filtering
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey E. Hinton · 2007
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Representational power of restricted Boltzmann machines and deep belief networks
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Classification using discriminative restricted Boltzmann machines
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Ruslan Salakhutdinov and Iain Murray · 2008
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Training restricted Boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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On the quantitative analysis of deep belief networks
Ruslan Salakhutdinov and Iain Murray · 2008
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Deep Boltzmann machines
Ruslan Salakhutdinov and Geoffrey E. Hinton · 2009
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Continuous relaxations for discrete Hamiltonian Monte Carlo
Yichuan Zhang, Zoubin Ghahramani, Amos J Storkey, and Charles A Sutton · 2012
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Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Auto-encoding variational Bayes
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Stochastic backpropagation and approximate inference in deep generative models
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Rényi divergence variational inference
Yingzhen Li and Richard E Turner · 2016
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Stochastic backpropagation through mixture density distributions
Alex Graves · 2016
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VAE learning via Stein variational gradient descent
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Grammar variational autoencoder
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Variational memory addressing in generative models
Jörg Bornschein, Andriy Mnih, Daniel Zoran, and Danilo Jimenez Rezende · 2017
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Supervised restricted Boltzmann machines
Tu Dinh Nguyen, Dinh Phung, Viet Huynh, and Trung Le · 2017
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Human-level concept learning through probabilistic program induction
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Discrete variational autoencoders
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Semi-supervised generation with cluster-aware generative models
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Evaluating the variance of likelihood-ratio gradient estimators
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Categorical reparametrization with gumble-softmax
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The concrete distribution: A continuous relaxation of discrete random variables
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REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
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A hierarchical latent vector model for learning long-term structure in music
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