Fetching the paper…
Reading the bibliography…
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Information processing in dynamical systems: Foundations of harmony theory
Smolensky, P. (1986) · 1986
Earlier work this paper cites.
The wake-sleep algorithm for unsupervised neural networks
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995) · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998) · 1998
Earlier work this paper cites.
On the convergence of Markovian stochastic algorithms with rapidly decreasing ergodicity rates
Younes, L. (1999) · 1999
Earlier work this paper cites.
Estimation of non-normalized statistical models using score matching
Hyvärinen, A. (2005) · 2005
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y. (2006) · 2006
Earlier work this paper cites.
Training restricted Boltzmann machines using approximations to the likelihood gradient
Tieleman, T. (2008) · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008) · 2008
Earlier work this paper cites.
Learning deep architectures for AI
Bengio, Y. (2009) · 2009
Cited alongside, same era.
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009) · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
Cited alongside, same era.
Deep Boltzmann machines
Salakhutdinov, R. and Hinton, G. E. (2009) · 2009
Cited alongside, same era.
Theano: a CPU and GPU math expression compiler
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., and Bengio, Y. (2010) · 2010
Cited alongside, same era.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvarinen, A. (2010) · 2010
Cited alongside, same era.
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A., Bouchard, N., and Bengio, Y. (2012) · 2012
Later among the works it cites.
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. (2012) · 2012
Later among the works it cites.
A generative process for sampling contractive auto-encoders
Rifai, S., Bengio, Y., Dauphin, Y., and Vincent, P. (2012) · 2012
Later among the works it cites.
Maxout networks
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013a) · 2013
Later among the works it cites.
Multi-prediction deep Boltzmann machines
Goodfellow, I. J., Mirza, M., Courville, A., and Bengio, Y. (2013b) · 2013
Later among the works it cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The Toronto face dataset
Susskind, J., Anderson, A., and Hinton, G. E. (2010) · 2010
Cited alongside, same era.
Deep sparse rectifier neural networks
Glorot, X., Bordes, A., and Bengio, Y. (2011) · 2011
Cited alongside, same era.
Better mixing via deep representations
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S. (2013a)
Cited in the paper.
Generalized denoising auto-encoders as generative models
Bengio, Y., Yao, L., Alain, G., and Vincent, P. (2013b)
Cited in the paper.
Deep generative stochastic networks trainable by backprop
Bengio, Y., Thibodeau-Laufer, E., and Yosinski, J. (2014a)
Cited in the paper.
Deep generative stochastic networks trainable by backprop
Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. (2014b)
Cited in the paper.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
Closest in time.
Quickly generating representative samples from an RBM-derived process
Breuleux, O., Bengio, Y., and Vincent, P. (2011) · 2073
Closest in time.