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We propose a novel method to directly learn a stochastic transition operator whose repeated application provides generated samples.
The helmholtz machine
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Path-ensemble averages in systems driven far from equilibrium
Crooks, G. E. (2000) · 2000
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Annealed importance sampling
Neal, R. M. (2001) · 2001
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006) · 2006
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Optimal finite-time processes in stochastic thermodynamics
Schmiedl, T. and Seifert, U. (2007) · 2007
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008) · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Deep boltzmann machines
Salakhutdinov, R. and Hinton, G. (2009) · 2009
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010) · 2010
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011) · 2011
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Thermodynamic metrics and optimal paths
Sivak, D. A. and Crooks, G. E. (2012) · 2012
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Generalized denoising auto-encoders as generative models
Bengio, Y., Yao, L., Alain, G., and Vincent, P. (2013b) · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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What regularized auto-encoders learn from the data-generating distribution
Alain, G. and Bengio, Y. (2014) · 2014
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Deep generative stochastic networks trainable by backprop
Bengio, Y., Thibodeau-Laufer, E. r., Alain, G., and Yosinski, J. (2014) · 2014
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Accurate and conservative estimates of MRF log-likelihood using reverse annealing
Burda, Y., Grosse, R. B., and Salakhutdinov, R. (2014) · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Theano: A python framework for fast computation of mathematical expressions
Al-Rfou, R., Alain, G., Almahairi, A., and et al. (2016) · 2016
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Ba, J. L., Kiros, J. R., and Hinton, G. E. (2016) · 2016
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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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Adam: A method for stochastic optimization
Kingma, D. and Ba, J. (2014) · 2014
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Random feedback weights support learning in deep neural networks
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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Why are deep nets reversible: a simple theory, with implications for training
Arora, S., Liang, Y., and Ma, T. (2015) · 2015
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An objective function for STDP
Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., and Wu, Y. (2015) · 2015
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Draw: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., and Wierstra, D. (2015) · 2015
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Near-optimal protocols in complex nonequilibrium transformations
Gingrich, T. R., Rotskoff, G. M., Crooks, G. E., and Geissler, P. L. (2016) · 2016
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Improving variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., and Welling, M. (2016) · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016) · 2016
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Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O. (2016) · 2016
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A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M. (2016) · 2016
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Learning to generate samples from noise through infusion training
Bordes, F., Honari, S., and Vincent, P. (2017) · 2017
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