Variational Wasserstein gradient flow

4 December 2021

Qinsheng Zhang

Papers citing "Variational Wasserstein gradient flow"

39 / 39 papers shown

Title
Nested Annealed Training Scheme for Generative Adversarial Networks Chang Wan Ming-Hsuan Yang Minglu Li Yunliang Jiang Zhonglong Zheng GAN 79 0 0 20 Jan 2025
Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions Shang Wu Yazhen Wang 74 0 0 11 Jan 2025
Importance Corrected Neural JKO Sampling Johannes Hertrich Robert Gruhlke 68 2 0 29 Jul 2024
Neural Optimal Transport Alexander Korotin Daniil Selikhanovych Evgeny Burnaev OT 165 94 0 28 Jan 2022
Generative Modeling with Optimal Transport Maps Litu Rout Alexander Korotin Evgeny Burnaev OT DiffM 166 66 0 06 Oct 2021
Proximal Optimal Transport Modeling of Population Dynamics Charlotte Bunne Laetitia Meng-Papaxanthos Andreas Krause Marco Cuturi OT 65 85 0 11 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis Yair Schiff Youssef Mroueh 53 54 0 01 Jun 2021
Large-Scale Wasserstein Gradient Flows Petr Mokrov Alexander Korotin Lingxiao Li Aude Genevay Justin Solomon Evgeny Burnaev 50 72 0 01 Jun 2021
Wasserstein Proximal of GANs A. Lin Wuchen Li Stanley Osher Guido Montúfar GAN 27 47 0 13 Feb 2021
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization Alexander Korotin Lingxiao Li Justin Solomon Evgeny Burnaev 96 51 0 02 Feb 2021
Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization Zhuoran Yang Yufeng Zhang Yongxin Chen Zhaoran Wang OT 53 5 0 21 Dec 2020
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang Ricky T. Q. Chen Christos Tsirigotis Aaron Courville OT 141 97 0 10 Dec 2020
f-Divergence Variational Inference Neng Wan Dapeng Li N. Hovakimyan 54 32 0 28 Sep 2020
A Framework For Contrastive Self-Supervised Learning And Designing A New Approach William Falcon Kyunghyun Cho SSL 49 104 0 31 Aug 2020
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks JiaoJiao Fan Amirhossein Taghvaei Yongxin Chen 50 58 0 08 Jul 2020
AE-OT-GAN: Training GANs from data specific latent distribution Dongsheng An Yang Guo Min Zhang Xin Qi Na Lei S. Yau X. Gu DRL GAN 34 24 0 11 Jan 2020
Wasserstein-2 Generative Networks Alexander Korotin Vage Egiazarian Arip Asadulaev Alexander Safin Evgeny Burnaev GAN 165 104 0 28 Sep 2019
Optimal transport mapping via input convex neural networks Ashok Vardhan Makkuva Amirhossein Taghvaei Sewoong Oh Jason D. Lee OT 19 199 0 28 Aug 2019
Universal Approximation with Deep Narrow Networks Patrick Kidger Terry Lyons 61 329 0 21 May 2019
Deep Generative Learning via Variational Gradient Flow Yuan Gao Yuling Jiao Yang Wang Yao Wang Can Yang Shunkang Zhang 82 36 0 24 Jan 2019
Glow: Generative Flow with Invertible 1x1 Convolutions Diederik P. Kingma Prafulla Dhariwal BDL DRL 201 3,110 0 09 Jul 2018
Approximate inference with Wasserstein gradient flows Charlie Frogner T. Poggio DiffM 8 32 0 12 Jun 2018
On gradient regularizers for MMD GANs Michael Arbel Danica J. Sutherland Mikolaj Binkowski Arthur Gretton 55 94 0 29 May 2018
Langevin Monte Carlo and JKO splitting Espen Bernton 46 79 0 23 Feb 2018
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem Andre Wibisono 77 178 0 22 Feb 2018
Spectral Normalization for Generative Adversarial Networks Takeru Miyato Toshiki Kataoka Masanori Koyama Yuichi Yoshida ODL 130 4,421 0 16 Feb 2018
Large-Scale Optimal Transport and Mapping Estimation Vivien Seguy B. Damodaran Rémi Flamary Nicolas Courty Antoine Rolet Mathieu Blondel OT 61 245 0 07 Nov 2017
Convergence of Langevin MCMC in KL-divergence Xiang Cheng Peter L. Bartlett 32 189 0 25 May 2017
Generalization and Equilibrium in Generative Adversarial Nets (GANs) Sanjeev Arora Rong Ge Yingyu Liang Tengyu Ma Yi Zhang GAN 35 687 0 02 Mar 2017
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications Tim Salimans A. Karpathy Xi Chen Diederik P. Kingma 54 933 0 19 Jan 2017
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 255 611 0 22 Sep 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 61 1,082 0 16 Aug 2016
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization Sebastian Nowozin Botond Cseke Ryota Tomioka GAN 86 1,648 0 02 Jun 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 249 7,951 0 23 May 2016
A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation Qiang Liu Jason D. Lee Michael I. Jordan 94 478 0 10 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.3K 192,638 0 10 Dec 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.1K 76,547 0 18 May 2015
Nonparametric variational inference S. Gershman Matt Hoffman David M. Blei BDL 79 153 0 18 Jun 2012
Estimating divergence functionals and the likelihood ratio by convex risk minimization X. Nguyen Martin J. Wainwright Michael I. Jordan 149 799 0 04 Sep 2008