Fetching the paper…
Reading the bibliography…
Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations.
Some extensions of W. Gautschi’s inequalities for the Gamma function
David Kershaw · 1983
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, Leon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
E-statistics: The energy of statistical samples
Gabor Székely · 2002
Earlier work this paper cites.
Support vector machines
Ingo Steinwart and Andreas Christmann · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Tractability of Multivariate Problems. Volume II , volume 12 of EMS Tracts in Mathematics
Erich Novak and Henryk Wozniakowski · 2010
Earlier work this paper cites.
Hilbert space embeddings and metrics on probability measures
Bharath K Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, and Gert RG Lanckriet · 2010
Earlier work this paper cites.
Universality, characteristic kernels and RKHS embedding of measures
Bharath K Sriperumbudur, Kenji Fukumizu, and Gert RG Lanckriet · 2011
Earlier work this paper cites.
Dithering by differences of convex functions
Tanja Teuber, Gabriele Steidl, Pascal Gwosdek, Christian Schmaltz, and Joachim Weickert · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee W Teh · 2011
Earlier work this paper cites.
Spherical harmonics and approximations on the unit sphere: an introduction , volume 2044
Kendall Atkinson and Weimin Han · 2012
Earlier work this paper cites.
Quadrature errors, discrepancies, and their relations to halftoning on the torus and the sphere
Manuel Gräf, Daniel Potts, and Gabriele Steidl · 2012
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2012
Earlier work this paper cites.
Unidimensional and evolution methods for optimal transportation
Nicolas Bonnotte · 2013
Earlier work this paper cites.
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton, and Kenji Fukumizu · 2013
Earlier work this paper cites.
Energy statistics: A class of statistics based on distances
Gábor J. Székely and Maria L. Rizzo · 2013
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.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Equivalence of gradient flows and entropy solutions for singular nonlocal interaction equations in 1D
Giovanni A Bonaschi, José A Carrillo, Marco Di Francesco, and Mark A Peletier · 2015
Earlier work this paper cites.
Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Entropic approximation of Wasserstein gradient flows
Gabriel Peyré · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
GANs trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Sub-sampled cubic regularization for non-convex optimization
Jonas Moritz Kohler and Aurelien Lucchi · 2017
Cited alongside, same era.
MMD GAN: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabas Poczos · 2017
Cited alongside, same era.
Stein variational gradient descent as gradient flow
Qiang Liu · 2017
Cited alongside, same era.
Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Annealed flow transport Monte Carlo
Michael Arbel, Alex Matthews, and Arnaud Doucet · 2021
Later among the works it cites.
Sliced iterative normalizing flows
Biwei Dai and Uros Seljak · 2021
Later among the works it cites.
KALE Flow: A relaxed KL gradient flow for probabilities with disjoint support
Pierre Glaser, Michael Arbel, and Arthur Gretton · 2021
Later among the works it cites.
A variational perspective on diffusion-based generative models and score matching
Chin-Wei Huang, Jae Hyun Lim, and Aaron Courville · 2021
Later among the works it cites.
A Cramer distance perspective on quantile regression based distributional reinforcement learning
Alix Lhéritier and Nicolas Bondoux · 2021
Later among the works it cites.
Large-scale Wasserstein gradient flows
Petr Mokrov, Alexander Korotin, Lingxiao Li, Aude Genevay, Justin M Solomon, and Evgeny Burnaev · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
Cited alongside, same era.
Concentration for Coulomb gases and Coulomb transport inequalities
Djalil Chafaï, Adrien Hardy, and Mylène Maïda · 2018
Cited alongside, same era.
Are GANs created equal? A large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
Cited alongside, same era.
Sobolev GAN
Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, and Yu Cheng · 2018
Cited alongside, same era.
Numerical Fourier Analysis
Gerlind Plonka, Daniel Potts, Gabriele Steidl, and Manfred Tasche · 2018
Cited alongside, same era.
High-dimensional probability: An introduction with applications in data science , volume 47
Roman Vershynin · 2018
Cited alongside, same era.
Later among the works it cites.
From optimal transport to discrepancy
Sebastian Neumayer and Gabriele Steidl · 2021
Later among the works it cites.
Distributional sliced-Wasserstein and applications to generative modeling
Khai Nguyen, Nhat Ho, Tung Pham, and Hung Bui · 2021
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Later among the works it cites.
Optimizing functionals on the space of probabilities with input convex neural networks
David Alvarez-Melis, Yair Schiff, and Youssef Mroueh · 2022
Later among the works it cites.
Matching normalizing flows and probability paths on manifolds
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Maximillian Nickel, Aditya Grover, Ricky T. Q. Chen, and Yaron Lipman · 2022
Later among the works it cites.
Proximal optimal transport modeling of population dynamics
Charlotte Bunne, Laetitia Papaxanthos, Andreas Krause, and Marco Cuturi · 2022
Later among the works it cites.
Neural variational gradient descent
Lauro Langosco di Langosco, Vincent Fortuin, and Heiko Strathmann · 2022
Later among the works it cites.
Variational Wasserstein gradient flow
Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei, and Yongxin Chen · 2022
Later among the works it cites.
Generalized sliced probability metrics
Soheil Kolouri, Kimia Nadjahi, Shahin Shahrampour, and Umut Şimşekli · 2022
Later among the works it cites.
Revisiting sliced Wasserstein on images: From vectorization to convolution
Khai Nguyen and Nhat Ho · 2022
Later among the works it cites.
Neural Wasserstein gradient flows for maximum mean discrepancies with Riesz kernels
Fabian Altekrüger, Johannes Hertrich, and Gabriele Steidl · 2023
Closest in time.
Particle-based variational inference with preconditioned functional gradient flow
Hanze Dong, Xi Wang, LIN Yong, and Tong Zhang · 2023
Closest in time.
Nonparametric generative modeling with conditional sliced-Wasserstein flows
Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, and Min Lin · 2023
Closest in time.
From optimization to sampling through gradient flows
Nicolas Garcia Trillos, Bamdad Hosseini, and Daniel Sanz-Alonso · 2023
Closest in time.
Generalized normalizing flows via Markov chains
Paul Lyonel Hagemann, Johannes Hertrich, and Gabriele Steidl · 2023
Closest in time.
Deep generative Wasserstein gradient flows
Alvin Heng, Abdul Fatir Ansari, and Harold Soh · 2023
Closest in time.
Characterization of translation invariant MMD on ℝ d \mathbb{R}^{d} and connections with Wasserstein distances
Thibault Modeste and Clément Dombry · 2023
Closest in time.
Hierarchical sliced Wasserstein distance
Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Minh Nguyen, and Nhat Ho · 2023
Closest in time.
Sliced optimal transport on the sphere
Michael Quellmalz, Robert Beinert, and Gabriele Steidl · 2023
Closest in time.
Controlling wasserstein distances by kernel norms with application to compressive statistical learning
Titouan Vayer and Rémi Gribonval · 2023
Closest in time.
Fast kernel summation in high dimensions via slicing and Fourier transforms
Johannes Hertrich · 2024
Closest in time.