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We present a new method for evaluating and training unnormalized density models.
On the anatomy of mcmc-based maximum likelihood learning of energy-based models
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On learning non-convergent short-run mcmc toward energy-based model
Nijkamp, E., Zhu, S.-C., and Wu, Y. N · 1904
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A bound for the error in the normal approximation to the distribution of a sum of dependent random variables
Stein, C. et al · 1972
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F · 1990
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y.-W · 2006
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Energy-based models in document recognition and computer vision
LeCun, Y., Chopra, S., Ranzato, M., and Huang, F.-J · 2007
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Synergistic face detection and pose estimation with energy-based models
Osadchy, M., Cun, Y. L., and Miller, M. L · 2007
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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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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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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Gaussian-bernoulli deep boltzmann machine
Cho, K. H., Raiko, T., and Ilin, A · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
Ramdas, A., Reddi, S. J., Póczos, B., Singh, A., and Wasserman, L · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2015
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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., and Jordan, M · 2016
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Operator variational inference
Ranganath, R., Tran, D., Altosaar, J., and Blei, D · 2016
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Generative models and model criticism via optimized maximum mean discrepancy
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Deep energy estimator networks
Saremi, S., Mehrjou, A., Schölkopf, B., and Hyvärinen, A · 2018
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Learning neural random fields with inclusive auxiliary generators
Song, Y. and Ou, Z · 2018
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Learning descriptor networks for 3d shape synthesis and analysis
Xie, J., Zheng, Z., Gao, R., Wang, W., Zhu, S.-C., and Nian Wu, Y · 2018
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Sutherland, D. J., Tung, H.-Y., Strathmann, H., De, S., Ramdas, A., Smola, A., and Gretton, A · 2016
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A theory of generative convnet
Xie, J., Lu, Y., Zhu, S.-C., and Wu, Y · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Measuring sample quality with kernels
Gorham, J. and Mackey, L · 2017
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Approximating spectral sums of large-scale matrices using stochastic chebyshev approximations
Han, I., Malioutov, D., Avron, H., and Shin, J · 2017
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Unbiased markov chain monte carlo with couplings
Jacob, P. E., O’Leary, J., and Atchadé, Y. F · 2017
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A linear-time kernel goodness-of-fit test
Jitkrittum, W., Xu, W., Szabó, Z., Fukumizu, K., and Gretton, A · 2017
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Minimum stein discrepancy estimators
Barp, A., Briol, F.-X., Duncan, A., Girolami, M., and Mackey, L · 2019
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Implicit generation and generalization in energy-based models
Du, Y. and Mordatch, I · 2019
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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 · 2019
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Your classifier is secretly an energy based model and you should treat it like one
Grathwohl, W., Wang, K.-C., Jacobsen, J.-H., Duvenaud, D., Norouzi, M., and Swersky, K · 2019
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Learning protein structure with a differentiable simulator
Ingraham, J., Riesselman, A., Sander, C., and Marks, D · 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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Understanding posterior collapse in generative latent variable models
Lucas, J., Tucker, G., Grosse, R., and Norouzi, M · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 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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The shape of data: Intrinsic distance for data distributions
Tsitsulin, A., Munkhoeva, M., Mottin, D., Karras, P., Bronstein, A., Oseledets, I., and Müller, E · 2019
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Learning energy-based spatial-temporal generative convnets for dynamic patterns
Xie, J., Zhu, S.-C., and Wu, Y. N · 2019
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Energy-based models for atomic-resolution protein conformations
Du, Y., Meier, J., Ma, J., Fergus, R., and Rives, A · 2020
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