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The recent introduction of thermodynamic integration techniques has provided a new framework for understanding and improving variational inference (VI).
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2019) · 1912
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On measures of entropy and information
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Maximum likelihood from incomplete data via the em algorithm
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A monte carlo method for high dimensional integration
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Markov chain Monte Carlo maximum likelihood
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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On the convergence of monte carlo maximum likelihood calculations
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Mixture models: theory, geometry and applications
Lindsay, B. G. (1995) · 1995
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Integral probability metrics and their generating classes of functions
Müller, A. (1997) · 1997
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Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
Gelman, A. and Meng, X.-L. (1998) · 1998
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Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences
Crooks, G. E. (1999) · 1999
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Understanding molecular simulation: from algorithms to applications
Frenkel, D. and Smit, B. (2001) · 2001
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Monte Carlo strategies in scientific computing
Liu, J. S. and Liu, J. S. (2001) · 2001
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Annealed importance sampling
Neal, R. M. (2001) · 2001
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Training products of experts by minimizing contrastive divergence
Hinton, G. E. (2002) · 2002
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Latent Dirichlet allocation
Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003) · 2003
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
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Computing bayes factors using thermodynamic integration
Lartillot, N. and Philippe, H. (2006) · 2006
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Variational free energy and the laplace approximation
Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., and Penny, W. (2007) · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
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On integral probability metrics, \ \backslash phi-divergences and binary classification
Sriperumbudur, B. K., Fukumizu, K., Gretton, A., Schölkopf, B., and Lanckriet, G. R. (2009) · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A. (2010) · 2010
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012) · 2012
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Bregman divergence as general framework to estimate unnormalized statistical models
Gutmann, M. and Hirayama, J.-i. (2012) · 2012
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Gutmann, M. U. and Hyvärinen, A. (2012) · 2012
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Evaluation of marginal likelihoods via the density of states
Habeck, M. (2012) · 2012
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Annealing between distributions by averaging moments
Grosse, R. B., Maddison, C. J., and Salakhutdinov, R. R. (2013) · 2013
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. (2013) · 2013
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Expectation propagation for approximate bayesian inference
Minka, T. P. (2013) · 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) · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D. (2014) · 2014
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Variational inference via chi upper bound minimization
Dieng, A. B., Tran, D., Ranganath, R., Paisley, J., and Blei, D. M. (2017) · 2017
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Model evidence from nonequilibrium simulations
Habeck, M. (2017) · 2017
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Adversarial variational Bayes: unifying variational autoencoders and generative adversarial networks
Mescheder, L., Nowozin, S., and Geiger, A. (2017) · 2017
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Parallel wavenet: Fast high-fidelity speech synthesis
Oord, A. v. d., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., Driessche, G. v. d., Lockhart, E., Cobo, L. C., Stimberg, F., et al. (2017) · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015) · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
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Markov chain Monte Carlo and variational inference: Bridging the gap
Salimans, T., Kingma, D., and Welling, M. (2015) · 2015
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Importance weighted autoencoders
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Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2016) · 2016
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Pu, Y., Gan, Z., Henao, R., Li, C., Han, S., and Carin, L. (2017) · 2017
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Tighter variational bounds are not necessarily better
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Sticking the landing: Simple, lower-variance gradient estimators for variational inference
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Style transfer from non-parallel text by cross-alignment
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On the quantitative analysis of decoder-based generative models
Wu, Y., Burda, Y., Salakhutdinov, R., and Grosse, R. (2017) · 2017
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Coupled variational bayes via optimization embedding
Dai, B., Dai, H., He, N., Liu, W., Liu, Z., Chen, J., Xiao, L., and Song, L. (2018) · 2018
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Categorical reparameterization with gumbel-softmax
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P. (2018) · 2018
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Large-scale celebfaces attributes (celeba) dataset
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Variational inference and model selection with generalized evidence bounds
Tao, C., Chen, L., Zhang, R., Henao, R., and Duke, L. C. (2018) · 2018
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VAE with a VampPrior
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Doubly reparameterized gradient estimators for monte carlo objectives
Tucker, G., Lawson, D., Gu, S., and Maddison, C. J. (2018) · 2018
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Implicit generation and modeling with energy based models
Du, Y. and Mordatch, I. (2019) · 2019
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The thermodynamic variational objective
Masrani, V., Le, T. A., and Wood, F. (2019) · 2019
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Learning non-convergent non-persistent short-run MCMC toward energy-based model
Nijkamp, E., Hill, M., Zhu, S.-C., and Wu, Y. N. (2019) · 2019
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Wasserstein dependency measure for representation learning
Ozair, S., Lynch, C., Bengio, Y., Oord, A. v. d., Levine, S., and Sermanet, P. (2019) · 2019
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Improved variational autoencoders for text modeling using dilated convolutions
Yang, Z., Hu, Z., Salakhutdinov, R., and Berg-Kirkpatrick, T. (2017) · 2019
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All in the exponential family: Bregman duality in thermodynamic variational inference
Brekelmans, R., Masrani, V., Wood, F., Steeg, G. V., and Galstyan, A. (2020) · 2020
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Supercharging imbalanced data learning with energy-based contrastive representation transfer
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Reconsidering generative objectives for counterfactual reasoning
Lu, D., Tao, C., Chen, J., Li, F., Guo, F., and Carin, L. (2020) · 2020
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f-divergence variational inference
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q-paths: Generalizing the geometric annealing path using power means
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