Fetching the paper…
Reading the bibliography…
We propose an Euler particle transport (EPT) approach for generative learning.
A general class of coefficients of divergence of one distribution from another
Syed Mumtaz Ali and Samuel D Silvey · 1966
Earlier work this paper cites.
Optimal global rates of convergence for nonparametric regression
Charles J. Stone · 1982
Earlier work this paper cites.
Logarithmic sobolev inequalities and stochastic ising models
Richard Holley and Daniel Stroock · 1987
Earlier work this paper cites.
Optimization and nonsmooth analysis , volume 5
Frank H Clarke · 1990
Earlier work this paper cites.
Polar factorization and monotone rearrangement of vector-valued functions
Yann Brenier · 1991
Earlier work this paper cites.
New problems on minimizing movements, boundary value problems for partial differential equations
E De Giorgi · 1993
Earlier work this paper cites.
Existence and uniqueness of monotone measure-preserving maps
Robert J. McCann · 1995
Earlier work this paper cites.
The variational formulation of the Fokker–Planck equation
Richard Jordan, David Kinderlehrer, and Felix Otto · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Calculus of variations
Izrail Moiseevitch Gelfand and Sergei Vasilevich Fomin · 2000
Earlier work this paper cites.
Generalization of an inequality by talagrand and links with the logarithmic sobolev inequality
F. Otto and C. Villani · 2000
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
The geometry of proper scoring rules
A Philip Dawid · 2007
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Gradient flows: in metric spaces and in the space of probability measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2008
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2008
Earlier work this paper cites.
Neural network learning: Theoretical foundations
Martin Anthony and Peter L Bartlett · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Introduction to Riemannian Manifolds
John Lee · 2010
Earlier work this paper cites.
Geometrical methods in the theory of ordinary differential equations , volume 250
Vladimir Igorevich Arnold · 2012
Earlier work this paper cites.
On the empirical estimation of integral probability metrics
Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert R. G Lanckriet · 2012
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.
Statistical analysis of distance estimators with density differences and density ratios
Takafumi Kanamori and Masashi Sugiyama · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
Cited alongside, same era.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Learning deep generative models
Ruslan Salakhutdinov · 2015
Cited alongside, same era.
Optimal transport for applied mathematicians
Filippo Santambrogio · 2015
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
On gradient regularizers for MMD GANs
Michael Arbel, Dougal Sutherland, Mikolaj Binkowski, and Arthur Gretton · 2018
Later among the works it cites.
Demystifying MMD GANs
Mikolaj Binkowski, Dougal J Sutherland, Michael Arbel, and Arthur Gretton · 2018
Later among the works it cites.
Generative modeling using the sliced wasserstein distance
Ishan Deshpande, Ziyu Zhang, and Alexander G Schwing · 2018
Later among the works it cites.
Learning generative models with sinkhorn divergences
Aude Genevay, Gabriel Peyre, and Marco Cuturi · 2018
Later among the works it cites.
Composite functional gradient learning of generative adversarial models
Rie Johnson and Tong Zhang · 2018
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow · 2016
Cited alongside, same era.
Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
Cited alongside, same era.
f f -GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Generative adversarial text to image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
Cited alongside, same era.
A two-step computation of the exact gan Wasserstein distance
Huidong Liu, GU Xianfeng, and Dimitris Samaras · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Later among the works it cites.
Chi-square generative adversarial network
Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, and Lawrence Carin Duke · 2018
Later among the works it cites.
Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, and B Schölkopf · 2018
Later among the works it cites.
High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
Later among the works it cites.
Monge-Ampère flow for generative modeling
Linfeng Zhang, Weinan E, and Lei Wang · 2018
Later among the works it cites.
Maximum mean discrepancy gradient flow
Michael Arbel, Anna Korba, Adil Salim, and Arthur Gretton · 2019
Later among the works it cites.
Nearly-tight vc-dimension and pseudodimension bounds for piecewise linear neural networks
Peter L Bartlett, Nick Harvey, Christopher Liaw, and Abbas Mehrabian · 2019
Later among the works it cites.
Deep generative learning via variational gradient flow
Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, and Shunkang Zhang · 2019
Later among the works it cites.
FFJORD: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2019
Later among the works it cites.
Sliced-Wasserstein autoencoder: An embarrassingly simple generative model
Soheil Kolouri, Phillip E Pope, Charles E Martin, and Gustavo K Rohde · 2019
Later among the works it cites.
Sliced-Wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter, Kamalika Chaudhuri, and Ruslan Salakhutdinov · 2019
Later among the works it cites.
Sinkhorn autoencoders
Giorgio Patrini, Samarth Bhargav, Rianne van den Berg, Max Welling, Patrick Forré, Tim Genewein, Marcello Carioni, KFU Graz, Frank Nielsen, and CSL Sony · 2019
Later among the works it cites.
Deep network approximation characterized by number of neurons
Zuowei Shen, Haizhao Yang, and Shijun Zhang · 2019
Later among the works it cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Later among the works it cites.
Wasserstein-Wasserstein auto-encoders
Shunkang Zhang, Yuan Gao, Yuling Jiao, Jin Liu, Yang Wang, and Can Yang · 2019
Later among the works it cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Closest in time.
Nonparametric regression using deep neural networks with relu activation function
Johannes Schmidt-Hieber · 2020
Closest in time.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Closest in time.