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The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable.
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Training products of experts by minimizing contrastive divergence
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Information theory and the central limit theorem
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Exponential family harmoniums with an application to information retrieval
Welling, M., Rosen-Zvi, M., and Hinton, G. E · 2004
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Hinton, G. E. and Salakhutdinov, R. R · 2006
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A fast learning algorithm for deep belief nets
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A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., and Huang, F · 2006
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Salakhutdinov, R · 2008
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On the quantitative analysis of deep belief networks
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Training restricted boltzmann machines using approximations to the likelihood gradient
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Tsybakov, A. B · 2008
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
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Deep Boltzmann machines
Salakhutdinov, R. and Hinton, G · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Regularized estimation of image statistics by score matching
Kingma, D. P. and LeCun, Y · 2010
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LeCun, Y., Cortes, C., and Burges, C · 2010
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On autoencoders and score matching for energy based models
Swersky, K., Ranzato, M., Buchman, D., Marlin, B. M., and de Freitas, N · 2011
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Multimodal learning with deep boltzmann machines
Srivastava, N. and Salakhutdinov, R · 2012
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Stein’s density approach and information inequalities
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Monte Carlo theory, methods and examples
Owen, A. B · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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NICE: non-linear independent components estimation
Deep energy estimator networks
Saremi, S., Mehrjou, A., Schölkopf, B., and Hyvärinen, A · 2018
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Cooperative training of descriptor and generator networks
Xie, J., Lu, Y., Gao, R., Zhu, S.-C., and Wu, Y. N · 2018
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Kernel exponential family estimation via doubly dual embedding
Dai, B., Dai, H., Gretton, A., Song, L., Schuurmans, D., and He, N · 2019
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Implicit generation and modeling with energy based models
Du, Y. and Mordatch, I · 2019
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Annealed denoising score matching: Learning energy-based models in high-dimensional spaces
Li, Z., Chen, Y., and Sommer, F. T · 2019
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Dinh, L., Krueger, D., and Bengio, Y · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Importance weighted autoencoders
Burda, Y., Grosse, R. B., and Salakhutdinov, R · 2016
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An overview of the estimation of large covariance and precision matrices, 2016
Fan, J., Liao, Y., and Liu, H · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Q. and Wang, D · 2016
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A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J. D., and Jordan, M. I · 2016
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Learning non-convergent non-persistent short-run MCMC toward energy-based model
Nijkamp, E., Hill, M., Zhu, S., and Wu, Y. N · 2019
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Variational noise-contrastive estimation
Rhodes, B. and Gutmann, M. U · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Sliced score matching: A scalable approach to density and score estimation
Song, Y., Garg, S., Shi, J., and Ermon, S · 2019
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On fenchel mini-max learning
Tao, C., Chen, L., Dai, S., Chen, J., Bai, K., Wang, D., Feng, J., Lu, W., Bobashev, G. V., and Carin, L · 2019
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Bi-level score matching for learning energy-based latent variable models
Bao, F., Li, C., Xu, T., Su, H., Zhu, J., and Zhang, B · 2020
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Flow contrastive estimation of energy-based models
Gao, R., Nijkamp, E., Kingma, D. P., Xu, Z., Dai, A. M., and Wu, Y. N · 2020
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Sliced kernelized stein discrepancy
Gong, W., Li, Y., and Hernández-Lobato, J. M · 2020
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Joint training of variational auto-encoder and latent energy-based model
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To relieve your headache of training an mrf, take advil
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Energy-based out-of-distribution detection
Liu, W., Wang, X., Owens, J. D., and Li, Y · 2020
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On the anatomy of mcmc-based maximum likelihood learning of energy-based models
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Efficient learning of generative models via finite-difference score matching
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Unbiased contrastive divergence algorithm for training energy-based latent variable models
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
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Score-based generative modeling through stochastic differential equations
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