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We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data.
Beitrag zur Theorie des Ferromagnetismus
Ising, E · 1925
Earlier work this paper cites.
Pulse code communication
Gray, F · 1953
Earlier work this paper cites.
Maximal flow through a network
Ford, L. R. and Fulkerson, D. R · 1956
Earlier work this paper cites.
Monte Carlo sampling methods using Markov chains and their applications
Hastings, W. K · 1970
Earlier work this paper cites.
Cluster Monte Carlo algorithms
Wang, J.-S. and Swendsen, R. H · 1990
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
Earlier work this paper cites.
Information Theory, Inference, and Learning Algorithms
MacKay, D. J. C · 2003
Earlier work this paper cites.
Learning to predict by the methods of temporal differences
Sutton, R. S · 2005
Earlier work this paper cites.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2005
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y. W · 2006
Earlier work this paper cites.
A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, A., and Huang, F. J · 2006
Earlier work this paper cites.
Training restricted Boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
Earlier work this paper cites.
Learning in Markov random fields using tempered transitions
Salakhutdinov, R · 2009
Earlier work this paper cites.
Deep Boltzmann machines
Salakhutdinov, R. and Hinton, G. E · 2009
Earlier work this paper cites.
Using fast weights to improve persistent contrastive divergence
Tieleman, T. and Hinton, G. E · 2009
Earlier work this paper cites.
Tempered Markov chain Monte Carlo for training of restricted Boltzmann machines
Desjardins, G., Courville, A. C., Bengio, Y., Vincent, P., and Delalleau, O · 2010
Earlier work this paper cites.
Generating text with recurrent neural networks
Sutskever, I., Martens, J., and Hinton, G. E · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
Welling, M. and Teh, Y. W · 2011
Cited alongside, same era.
A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
Cited alongside, same era.
Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S · 2013
Cited alongside, same era.
Generating sequences with recurrent neural networks
Graves, A · 2013
Cited alongside, same era.
MADE: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Cited alongside, same era.
A complete recipe for stochastic gradient MCMC
Ma, Y., Ma, Y.-A., Chen, T., and Fox, E. B · 2015
Cited alongside, same era.
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Grathwohl, W., Chen, R. T. Q., Bettencourt, J., Sutskever, I., and Duvenaud, D. K · 2019
Later among the works it cites.
Learning non-convergent non-persistent short-run mcmc toward energy-based model
Nijkamp, E., Hill, M., Zhu, S.-C., and Wu, Y. N · 2019
Later among the works it cites.
Informed proposals for local mcmc in discrete spaces
Zanella, G · 2019
Later among the works it cites.
Interference and generalization in temporal difference learning
Bengio, E., Pineau, J., and Precup, D · 2020
Later among the works it cites.
Learning discrete energy-based models via auxiliary-variable local exploration
Dai, H., Singh, R., Dai, B., Sutton, C., and Schuurmans, D · 2020
Later among the works it cites.
Stein variational inference for discrete distributions
Han, J., Ding, F., Liu, X., Torresani, L., Peng, J., and Liu, Q · 2020
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Training deep energy-based models with f-divergence minimization
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Generalized energy based models
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Improved contrastive divergence training of energy based models
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Learning energy-based models by diffusion recovery likelihood
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How to train your energy-based models
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Biological sequence design with gflownets
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