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We consider the problem of learning deep generative models from data.
A learning algorithm for boltzmann machines
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J · 1985
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Connectionist learning of belief networks
Neal, R. M · 1992
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The “wake-sleep” algorithm for unsupervised neural networks
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M · 1995
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Bayesian neural networks and density networks
MacKay, D. J · 1995
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Neural networks for density estimation
Magdon-Ismail, M. and Atiya, A · 1998
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., et al · 2007
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A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K. M., Rasch, M., Schölkopf, B., and Smola, A. J · 2007
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2007
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Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
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Deep boltzmann machines
Salakhutdinov, R. and Hinton, G. E · 2009
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Kernel choice and classifiability for rkhs embeddings of probability distributions
Sriperumbudur, B. K., Fukumizu, K., Gretton, A., Lanckriet, G. R., and Schölkopf, B · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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The toronto face dataset
Susskind, J., Anderson, A., and Hinton, G. E · 2010
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Deconvolutional networks
Zeiler, M. D., Krishnan, D., Taylor, G. W., and Fergus, R · 2010
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The neural autoregressive distribution estimator
Larochelle, H. and Murray, I · 2011
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Stacked convolutional auto-encoders for hierarchical feature extraction
Masci, J., Meier, U., Cireşan, D., and Schmidhuber, J · 2011
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Contractive auto-encoders: Explicit invariance during feature extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Towards end-to-end speech recognition with recurrent neural networks
Graves, A. and Jaitly, N · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Unifying visual-semantic embeddings with multimodal neural language models
Kiros, R., Salakhutdinov, R., and Zemel, R. S · 2014
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A winner-take-all method for training sparse convolutional autoencoders
Makhzani, A. and Frey, B · 2014
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Neural variational inference and learning in belief networks
Mnih, A. and Gregor, K · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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A generative process for sampling contractive auto-encoders
Rifai, S., Bengio, Y., Dauphin, Y., and Vincent, P · 2012
Cited alongside, same era.
Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P · 2012
Cited alongside, same era.
Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C · 2013
Cited alongside, same era.
Deep generative stochastic networks trainable by backprop
Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J · 2014
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Cited alongside, same era.
From captions to visual concepts and back
Fang, H., Gupta, S., Iandola, F., Srivastava, R., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J., Zitnick, C. L., and Zweig, G · 2014
Cited alongside, same era.
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y · 2014
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Input warping for bayesian optimization of non-stationary functions
Snoek, J., Swersky, K., Zemel, R. S., and Adams, R. P · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2014
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Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D · 2014
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Fastmmd: Ensemble of circular discrepancy for efficient two-sample test
Zhao, J. and Meng, D · 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., Poczos, B., Singh, A., and Wasserman, L · 2015
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