Training generative neural networks via Maximum Mean Discrepancy optimization

“Dimensionality-Reduction Using Connectionist Networks.”

Eric Saund · 1989

Earlier work this paper cites.

“Bayesian Neural Networks and Density Networks”

David˜J.C. MacKay · 1994

Earlier work this paper cites.

“Gradient-based learning applied to document recognition”

Yann Lecun, LŽon Bottou, Yoshua Bengio and Patrick Haffner · 1998

Earlier work this paper cites.

“Foundations of modern probability”

Olav Kallenberg · 2002

Earlier work this paper cites.

“A Few Notes on Statistical Learning Theory”

Shahar Mendelson · 2003

Earlier work this paper cites.

“Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)”

Carl˜Edward Rasmussen and Christopher K.˜I. Williams · 2005

Cited alongside, same era.

“Reducing the dimensionality of data with neural networks”

G˜E Hinton and R˜R Salakhutdinov · 2006

Cited alongside, same era.

“Injective Hilbert Space Embeddings of Probability Measures”

Bharath˜K. Sriperumbudur et al · 2008

Cited alongside, same era.

“Deep Boltzmann Machines”

Ruslan Salakhutdinov and Geoffrey˜E. Hinton · 2009

Cited alongside, same era.

“The Toronto face database”, 2010

J.˜M. Susskind, A.˜K. Anderson and G.˜E. Hinton · 2010

Cited alongside, same era.

“A Kernel Two-sample Test”

Arthur Gretton et al · 2012

Later among the works it cites.

“Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude”, COURSERA: Neural Networks for Machine Learning, 2012

T. Tieleman and G. Hinton · 2012

Later among the works it cites.

“A Deep and Tractable Density Estimator.”

Original

Benigno Uria, Iain Murray and Hugo Larochelle · 2013

Later among the works it cites.

“Generative Adversarial Nets”, 2014

Ian˜J. Goodfellow et al · 2014

Later among the works it cites.

“Generative Moment Matching Networks”, http://arxiv.org/abs/1502.02761v1, 2015

Original

Yujia Li, Kevin Swersky and Richard Zemel · 2015

Closest in time.