Fetching the paper…
Reading the bibliography…
A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network.
Mathematical theory of optimal processes
Lev Semenovich Pontryagin · 1962
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Some practical Runge-Kutta formulas
Lawrence F Shampine · 1986
Earlier work this paper cites.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
M.F. Hutchinson · 1989
Earlier work this paper cites.
Nonlinear Systems
H.K. Khalil · 2002
Earlier work this paper cites.
A general-purpose software framework for dynamic optimization
Joel Andersson · 2013
Earlier work this paper cites.
NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Cited alongside, same era.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Estimating the Spectral Density of Large Implicit Matrices
R. P. Adams, J. Pennington, M. J. Johnson, J. Smith, Y. Ovadia, B. Patton, and J. Saunderson · 2018
Closest in time.
Sylvester normalizing flows for variational inference
Rianne van den Berg, Leonard Hasenclever, Jakub M Tomczak, and Max Welling · 2018
Closest in time.
Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Closest in time.
Neural autoregressive flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
Closest in time.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
Closest in time.
Spectral normalization for generative adversarial networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
George Papamakarios, Iain Murray, and Theo Pavlakou · 2017
Cited alongside, same era.
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Closest in time.
Transformation autoregressive networks
Junier B Oliva, Avinava Dubey, Barnabás Póczos, Jeff Schneider, and Eric P Xing · 2018
Closest in time.