Deep Tempering

Information processing in dynamical systems: Foundations of harmony theory

Smolensky, P. (1986) · 1986

Earlier work this paper cites.

Nonuniversal critical dynamics in monte carlo simulations

Swendsen, R. H. and Wang, J.-S. (1987) · 1987

Earlier work this paper cites.

On the convergence of Markovian stochastic algorithms with rapidly decreasing ergodicity rates

Original

Younes, L. (1999) · 1999

Earlier work this paper cites.

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., and Teh, Y. (2006) · 2006

Earlier work this paper cites.

On the quantitative analysis of deep belief networks

Salakhutdinov, R. and Murray, I. (2008) · 2008

Cited alongside, same era.

Training restricted Boltzmann machines using approximations to the likelihood gradient

Tieleman, T. (2008) · 2008

Cited alongside, same era.

Deep Boltzmann machines

Salakhutdinov, R. and Hinton, G. (2009) · 2009

Cited alongside, same era.

Parallel tempering is efficient for learning restricted Boltzmann machines

Cho, K., Raiko, T., and Ilin, A. (2010) · 2010

Cited alongside, same era.

Adaptive parallel tempering for stochastic maximum likelihood learning of rbms

Desjardins, G., Courville, A., and Bengio, Y. (2010a)

Cited in the paper.

Learning deep Boltzmann machines using adaptive MCMC

Salakhutdinov, R. (2010a)

Cited in the paper.

Learning in Markov random fields using tempered transitions

Salakhutdinov, R. (2010b)

Cited in the paper.

Tempered Markov chain Monte Carlo for training of restricted Boltzmann machine

Desjardins, G., Courville, A., and Bengio, Y. (2010b) · 2010

Later among the works it cites.

Better mixing via deep representations

Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S. (2013) · 2013

Later among the works it cites.

Texture modeling with convolutional spike-and-slab RBMs and deep extensions

Luo, H., Carrier, P. L., Courville, A., and Bengio, Y. (2013) · 2013

Later among the works it cites.