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Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient.
On the convergence of Markovian stochastic algorithms with rapidly decreasing ergodicity rates
Younes, L · 1998
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2000
Earlier work this paper cites.
The im algorithm: A variational approach to information maximization
Barber, D. and Agakov, F · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y. W · 2006
Earlier work this paper cites.
A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M.-A., and Huang, F.-J · 2006
Earlier work this paper cites.
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H · 2007
Earlier work this paper cites.
Training restricted Boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
Earlier work this paper cites.
Learning deep architectures for AI
Bengio, Y · 2009
Earlier work this paper cites.
Exploring strategies for training deep neural networks
Larochelle, H., Bengio, Y., Louradour, J., and Lamblin, P · 2009
Earlier work this paper cites.
Deep boltzmann machines
Salakhutdinov, R. and Hinton, G · 2009
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvarinen, A · 2010
Earlier work this paper cites.
Riemann manifold langevin and hamiltonian monte carlo methods
Girolami, M. and Calderhead, B · 2011
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Vincent, P · 2011
Earlier work this paper cites.
Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S · 2013
Earlier work this paper cites.
Uci machine learning repository, 2013
Lichman, M. et al · 2013
Cited alongside, same era.
On distinguishability criteria for estimating generative models
Goodfellow, I · 2014
Cited alongside, same era.
Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Calibrating energy-based generative adversarial networks
Dai, Z., Almahairi, A., Bachman, P., Hovy, E., and Courville, A · 2017
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Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017
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The numerics of gans
Mescheder, L., Nowozin, S., and Geiger, A · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
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Theis, L., Oord, A. v. d., and Bethge, M · 2015
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, A · 2016
Cited alongside, same era.
Deep directed generative models with energy-based probability estimation
Kim, T. and Bengio, Y · 2016
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Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Cited alongside, same era.
Pixel recurrent neural networks
van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Deep structured energy based models for anomaly detection
Zhai, S., Cheng, Y., Lu, W., and Zhang, Z · 2016
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Srivastava, A., Valkoz, L., Russell, C., Gutmann, M. U., and Sutton, C · 2017
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Mine: mutual information neural estimation
Belghazi, I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, R. D · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Trischler, A., and Bengio, Y · 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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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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On variational lower bounds of mutual information
Poole, B., Ozair, S., Oord, A. v. d., Alemi, A., and Tucker, G · 2018
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Efficient gan-based anomaly detection
Zenati, H., Foo, C. S., Lecouat, B., Manek, G., and Chandrasekhar, V. R · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H · 2018
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