Fetching the paper…
Reading the bibliography…
Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function.
Information processing in dynamical systems: foundations of harmony theory
P. Smolensky · 1986
Earlier work this paper cites.
Connectionist learning of belief networks
Radford M. Neal · 1992
Earlier work this paper cites.
Sampling from multimodal distributions using tempered transitions
Radford Neal · 1996
Earlier work this paper cites.
Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Christopher Jarzynski · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Annealed importance sampling
R. M. Neal · 2001
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E. Hinton · 2002
Earlier work this paper cites.
Sequential Monte Carlo samplers
P. del Moral, A. Doucet, and A. Jasra · 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.
Simulation
S. M. Ross · 2006
Cited alongside, same era.
Studies in lower bounding probability of evidence using the Markov inequality
V. Gogate, B. Bidyuk, and R. Dechter · 2007
Cited alongside, same era.
On the quantitative analysis of deep belief networks
Ruslan Salakhutdinov and Ian Murray · 2008
Cited alongside, same era.
Training restricted Boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
Cited alongside, same era.
Estimating Bayes factors via thermodynamic integration and population MCMC
Ben Calderhead and Mark Girolami · 2009
Cited alongside, same era.
Deep Boltzmann machines
Ruslan Salakhutdinov and Geoffrey E. Hinton · 2009
Cited alongside, same era.
In all likelihood, deep belief is not enough
Lucas Theis, Sebastian Gerwinn, Fabian Sinz, and Matthias Bethge · 2011
Later among the works it cites.
Tuning tempered transitions
Gundula Behrens, Nial Friel, and Merrilee Hurn · 2012
Later among the works it cites.
Bounding the test log-likelihood of generative models
Y. Bengio, L. Yao, and K. Cho · 2013
Later among the works it cites.
Annealing between distributions by averaging moments
R. B. Grosse, C. J. Maddison, and R. Salakhutdinov · 2013
Later among the works it cites.
One-shot learning by inverting a compositional causal process
Brenden M Lake, Ruslan Salakhutdinov, and Josh Tenenbaum · 2013
Later among the works it cites.
Deep autoregressive networks
K. Gregor, I. Danihelka, A. Mnih, C. Blundell, and D. Wierstra · 2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The neural autoregressive distribution estimator
H. Larochelle and I. Murray · 2011
Cited alongside, same era.
Sum-product networks: a new deep architecture
H. Poon and P. Domingos · 2011
Cited alongside, same era.
Neural variational inference and learning in belief networks
A. Mnih and K. Gregor · 2014
Closest in time.
Black box variational inference
R. Ranganath, S. Gerrish, and D. M. Blei · 2014
Closest in time.