Fetching the paper…
Reading the bibliography…
Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probability distribution defined by the network weights.
A general class of coefficients of divergence of one distribution from another
S. M. Ali and S. D. Silvey · 1966
Earlier work this paper cites.
Mixture density networks
C. M. Bishop · 1994
Earlier work this paper cites.
Bayesian neural networks and density networks
D. J. C. MacKay · 1995
Earlier work this paper cites.
GTM: The generative topographic mapping
C. M. Bishop, M. Svensén, and C. K. I. Williams · 1998
Earlier work this paper cites.
Information theory and statistics: A tutorial
I. Csiszár and P. C. Shields · 2004
Earlier work this paper cites.
Divergence measures and message passing
T. Minka · 2005
Earlier work this paper cites.
On divergences and informations in statistics and information theory
F. Liese and I. Vajda · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
T. Gneiting and A. E. Raftery · 2007
Earlier work this paper cites.
A kernel statistical test of independence
A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf, and A. J. Smola · 2007
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
M. Gutmann and A. Hyvärinen · 2010
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by convex risk minimization
X. Nguyen, M. J. Wainwright, and M. I. Jordan · 2010
Earlier work this paper cites.
Hilbert space embeddings and metrics on probability measures
B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. Lanckriet · 2010
Cited alongside, same era.
The neural autoregressive distribution estimator
H. Larochelle and I. Murray · 2011
Cited alongside, same era.
Fundamentals of convex analysis
J. B. Hiriart-Urruty and C. Lemaréchal · 2012
Cited alongside, same era.
Generating sequences with recurrent neural networks
A. Graves · 2013
Cited alongside, same era.
Auto-encoding variational Bayes
D. P. Kingma and M. Welling · 2013
Cited alongside, same era.
RNADE: The real-valued neural autoregressive density-estimator
B. Uria, I. Murray, and H. Larochelle · 2013
Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
Later among the works it cites.
Fast and accurate deep network learning by exponential linear units (ELUs)
D. A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
Later among the works it cites.
Training generative neural networks via maximum mean discrepancy optimization
G. K. Dziugaite, D. M. Roy, and Z. Ghahramani · 2015
Later among the works it cites.
How (not) to train your generative model: scheduled sampling, likelihood, adversary?
F. Huszár · 2015
Later among the works it cites.
Generative moment matching networks
Y. Li, K. Swersky, and R. Zemel · 2015
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.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Y. N. Dauphin, R. Pascanu, C. Gulcehre, K. Cho, S. Ganguli, and Y. Bengio · 2014
Cited alongside, same era.
Conditional generative adversarial nets for convolutional face generation
J. Gauthier · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
Cited alongside, same era.
On the chi-square and higher-order chi distances for approximating f-divergences
F. Nielsen and R. Nock · 2014
Cited alongside, same era.
An alternative update rule for generative adversarial networks
F. Huszár
Cited in the paper.
A. Makhzani, J. Shlens, N. Jaitly, and I. Goodfellow · 2015
Later among the works it cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Later among the works it cites.
Deep unsupervised learning using non-equilibrium thermodynamics
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Later among the works it cites.
A note on the evaluation of generative models
L. Theis, A. v.d. Oord, and M. Bethge · 2015
Later among the works it cites.
Chainer: a next-generation open source framework for deep learning
S. Tokui, K. Oono, S. Hido, and J. Clayton · 2015
Later among the works it cites.
LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
Later among the works it cites.