Fetching the paper…
Reading the bibliography…
Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly.
On the probable errors of frequency-constants
Edgeworth, Francis Ysidro · 1908
Earlier work this paper cites.
On an absolute criterion for fitting frequency curves
Fisher, Ronald A · 1912
Earlier work this paper cites.
Studies in linear and non-linear programming
Arrow, Kenneth Joseph, Hurwicz, Leonid, Uzawa, Hirofumi, and Chenery, Hollis Burnley · 1958
Earlier work this paper cites.
Statistical inference for probabilistic functions of finite state markov chains
Baum, Leonard E and Petrie, Ted · 1966
Earlier work this paper cites.
Statistical analysis of non-lattice data
Besag, Julian · 1975
Earlier work this paper cites.
Monte carlo methods of inference for implicit statistical models
Diggle, Peter J and Gratton, Richard J · 1984
Earlier work this paper cites.
Mixture Distributions—I
Everitt, Brian S · 1985
Earlier work this paper cites.
Learning and releaming in boltzmann machines
Hinton, Geoffrey E and Sejnowski, Terrence J · 1986
Earlier work this paper cites.
Nash and correlated equilibria: Some complexity considerations
Gilboa, Itzhak and Zemel, Eitan · 1989
Earlier work this paper cites.
Connectionist learning of belief networks
Neal, Radford M · 1992
Earlier work this paper cites.
Learning factorial codes by predictability minimization
Schmidhuber, Jürgen · 1992
Earlier work this paper cites.
Bayesian neural networks and density networks
MacKay, David JC · 1995
Earlier work this paper cites.
Integral probability metrics and their generating classes of functions
Müller, Alfred · 1997
Earlier work this paper cites.
GTM: The generative topographic mapping
Bishop, Christopher M, Svensén, Markus, and Williams, Christopher KI · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
Jordan, Michael I, Ghahramani, Zoubin, Jaakkola, Tommi S, and Saul, Lawrence K · 1999
Earlier work this paper cites.
Modeling high-dimensional discrete data with multi-layer neural networks
Bengio, Yoshua and Bengio, Samy · 2000
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, Geoffrey E · 2002
Earlier work this paper cites.
A new learning algorithm for mean field boltzmann machines
Welling, Max and Hinton, Geoffrey · 2002
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, Aapo · 2005
Earlier work this paper cites.
A kernel method for the two-sample-problem
Gretton, Arthur, Borgwardt, Karsten M, Rasch, Malte, Schölkopf, Bernhard, and Smola, Alex J · 2007
Cited alongside, same era.
Learning generative models via discriminative approaches
Tu, Zhuowen · 2007
Cited alongside, same era.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, Michael and Hyvärinen, Aapo · 2010
Cited alongside, same era.
The neural autoregressive distribution estimator
Larochelle, Hugo and Murray, Iain · 2011
Cited alongside, same era.
Better mixing via deep representations
Bengio, Yoshua, Mesnil, Grégoire, Dauphin, Yann, and Rifai, Salah · 2013
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, Diederik P and Welling, Max · 2013
Fast k-nearest neighbour search via Dynamic Continuous Indexing
Li, Ke and Malik, Jitendra · 2016
Later among the works it cites.
Learning in implicit generative models
Mohamed, Shakir and Lakshminarayanan, Balaji · 2016
Later among the works it cites.
f-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, Sebastian, Cseke, Botond, and Tomioka, Ryota · 2016
Later among the works it cites.
Improved techniques for training GANs
Salimans, Tim, Goodfellow, Ian, Zaremba, Wojciech, Cheung, Vicki, Radford, Alec, and Chen, Xi · 2016
Later among the works it cites.
Pixel recurrent neural networks
van den Oord, Aaron, Kalchbrenner, Nal, and Kavukcuoglu, Koray · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep generative stochastic networks trainable by backprop
Bengio, Yoshua, Laufer, Eric, Alain, Guillaume, and Yosinski, Jason · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Cited alongside, same era.
Likelihood-free inference via classification
Gutmann, Michael U, Dutta, Ritabrata, Kaski, Samuel, and Corander, Jukka · 2014
Cited alongside, same era.
Stochastic backpropagation and variational inference in deep latent gaussian models
Rezende, Danilo Jimenez, Mohamed, Shakir, and Wierstra, Daan · 2014
Cited alongside, same era.
Importance weighted autoencoders
Burda, Yuri, Grosse, Roger, and Salakhutdinov, Ruslan · 2015
Cited alongside, same era.
Training generative neural networks via maximum mean discrepancy optimization
Dziugaite, Gintare Karolina, Roy, Daniel M, and Ghahramani, Zoubin · 2015
Cited alongside, same era.
Wu, Yuhuai, Burda, Yuri, Salakhutdinov, Ruslan, and Grosse, Roger · 2016
Later among the works it cites.
Energy-based generative adversarial network
Zhao, Junbo, Mathieu, Michael, and LeCun, Yann · 2016
Later among the works it cites.
Towards principled methods for training generative adversarial networks
Arjovsky, Martin and Bottou, Léon · 2017
Later among the works it cites.
Wasserstein generative adversarial networks
Arjovsky, Martin, Chintala, Soumith, and Bottou, Léon · 2017
Later among the works it cites.
Do GANs actually learn the distribution? an empirical study
Arora, Sanjeev and Zhang, Yi · 2017
Later among the works it cites.
Generalization and equilibrium in generative adversarial nets (GANs)
Arora, Sanjeev, Ge, Rong, Liang, Yingyu, Ma, Tengyu, and Zhang, Yi · 2017
Later among the works it cites.
Flow-GAN: Bridging implicit and prescribed learning in generative models
Grover, Aditya, Dhar, Manik, and Ermon, Stefano · 2017
Later among the works it cites.
Boundary-seeking generative adversarial networks
Hjelm, R Devon, Jacob, Athul Paul, Che, Tong, Cho, Kyunghyun, and Bengio, Yoshua · 2017
Later among the works it cites.
Fast k-nearest neighbour search via Prioritized DCI
Li, Ke and Malik, Jitendra · 2017
Later among the works it cites.
Dualing GANs
Li, Yujia, Schwing, Alexander, Wang, Kuan-Chieh, and Zemel, Richard · 2017
Later among the works it cites.
The numerics of GANs
Mescheder, Lars, Nowozin, Sebastian, and Geiger, Andreas · 2017
Later among the works it cites.
Non-parametric estimation of jensen-shannon divergence in generative adversarial network training
Sinn, Mathieu and Rawat, Ambrish · 2017
Later among the works it cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, Jun-Yan, Park, Taesung, Isola, Phillip, and Efros, Alexei A · 2017
Later among the works it cites.