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Efficient Gradient Flows in Sliced-Wasserstein Space

21 October 2021

Papers citing "Efficient Gradient Flows in Sliced-Wasserstein Space"

48 / 48 papers shown

Title
Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions Shang Wu Yazhen Wang 88 0 0 11 Jan 2025
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance Ilana Sebag Muni Sreenivas Pydi Jean-Yves Franceschi Alain Rakotomamonjy Mike Gartrell Jamal Atif Alexandre Allauzen 89 3 0 13 Dec 2023
Measure transfer via stochastic slicing and matching Shiying Li Caroline Moosmueller 75 3 0 11 Jul 2023
Generative Adversarial Networks Gilad Cohen Raja Giryes GAN 298 30,152 0 01 Mar 2022
Variational Wasserstein gradient flow JiaoJiao Fan Qinsheng Zhang Amirhossein Taghvaei Yongxin Chen 146 57 0 04 Dec 2021
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework Chao Zhang Zhijian Li Hui Qian Xin Du 48 10 0 02 Dec 2021
Input Convex Gradient Networks Jack Richter-Powell Jonathan Lorraine Brandon Amos 65 15 0 23 Nov 2021
Generative Modeling with Optimal Transport Maps Litu Rout Alexander Korotin Evgeny Burnaev OT DiffM 190 70 0 06 Oct 2021
Relative Entropy Gradient Sampler for Unnormalized Distributions Xingdong Feng Yuan Gao Jian Huang Yuling Jiao Xu Liu 80 7 0 06 Oct 2021
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections Kimia Nadjahi Alain Durmus Pierre E. Jacob Roland Badeau Umut Simsekli 58 38 0 29 Jun 2021
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support Pierre Glaser Michael Arbel Arthur Gretton 94 40 0 16 Jun 2021
Proximal Optimal Transport Modeling of Population Dynamics Charlotte Bunne Laetitia Meng-Papaxanthos Andreas Krause Marco Cuturi OT 70 93 0 11 Jun 2021
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark Alexander Korotin Lingxiao Li Aude Genevay Justin Solomon Alexander N. Filippov Evgeny Burnaev OT 153 89 0 03 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks David Alvarez-Melis Yair Schiff Youssef Mroueh 80 57 0 01 Jun 2021
Large-Scale Wasserstein Gradient Flows Petr Mokrov Alexander Korotin Lingxiao Li Aude Genevay Justin Solomon Evgeny Burnaev 74 75 0 01 Jun 2021
Kernel Stein Discrepancy Descent Anna Korba Pierre-Cyril Aubin-Frankowski Szymon Majewski Pierre Ablin 61 52 0 20 May 2021
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Sam Bond-Taylor Adam Leach Yang Long Chris G. Willcocks VLM TPM 137 504 0 08 Mar 2021
Wasserstein Proximal of GANs A. Lin Wuchen Li Stanley Osher Guido Montúfar GAN 46 47 0 13 Feb 2021
Projected Wasserstein gradient descent for high-dimensional Bayesian inference Yifei Wang Peng Chen Wuchen Li 54 26 0 12 Feb 2021
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization Chin-Wei Huang Ricky T. Q. Chen Christos Tsirigotis Aaron Courville OT 179 98 0 10 Dec 2020
Sliced Iterative Normalizing Flows B. Dai U. Seljak 68 37 0 01 Jul 2020
Statistical and Topological Properties of Sliced Probability Divergences Kimia Nadjahi Alain Durmus Lénaïc Chizat Soheil Kolouri Shahin Shahrampour Umut Simsekli 53 86 0 12 Mar 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 213 1,717 0 05 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 553 42,639 0 03 Dec 2019
$On approximating $\nabla f$ with neural networks$ On approximating $\nabla f$ with neural networks Saeed Saremi FedML 51 22 0 28 Oct 2019
Wasserstein-2 Generative Networks Alexander Korotin Vage Egiazarian Arip Asadulaev Alexander Safin Evgeny Burnaev GAN 202 109 0 28 Sep 2019
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance Kimia Nadjahi Alain Durmus Umut Simsekli Roland Badeau MedIm 56 63 0 11 Jun 2019
Maximum Mean Discrepancy Gradient Flow Michael Arbel Anna Korba Adil Salim Arthur Gretton 116 164 0 11 Jun 2019
Accelerating Langevin Sampling with Birth-death Yulong Lu Jianfeng Lu J. Nolen 95 55 0 23 May 2019
Max-Sliced Wasserstein Distance and its use for GANs Ishani Deshpande Yuan-Ting Hu Ruoyu Sun A. Pyrros Nasir Siddiqui Oluwasanmi Koyejo Zhizhen Zhao David A. Forsyth Alex Schwing GAN 51 201 0 11 Apr 2019
Orthogonal Estimation of Wasserstein Distances Mark Rowland Jiri Hron Yunhao Tang K. Choromanski Tamás Sarlós Adrian Weller 71 43 0 09 Mar 2019
Generalized Sliced Wasserstein Distances Soheil Kolouri Kimia Nadjahi Umut Simsekli Roland Badeau Gustavo K. Rohde 50 301 0 01 Feb 2019
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design Jonathan Ho Xi Chen A. Srinivas Yan Duan Pieter Abbeel DRL 88 451 0 01 Feb 2019
Subspace Robust Wasserstein Distances François-Pierre Paty Marco Cuturi 167 155 0 25 Jan 2019
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions Antoine Liutkus Umut Simsekli Szymon Majewski Alain Durmus Fabian-Robert Stöter DiffM 93 122 0 21 Jun 2018
Approximate inference with Wasserstein gradient flows Charlie Frogner T. Poggio DiffM 22 32 0 12 Jun 2018
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem Andre Wibisono 111 183 0 22 Feb 2018
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 285 8,928 0 25 Aug 2017
Masked Autoregressive Flow for Density Estimation George Papamakarios Theo Pavlakou Iain Murray 217 1,360 0 19 May 2017
Stein Variational Gradient Descent as Gradient Flow Qiang Liu OT 98 277 0 25 Apr 2017
Towards Principled Methods for Training Generative Adversarial Networks Martín Arjovsky M. Nault GAN 83 2,112 0 17 Jan 2017
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 282 625 0 22 Sep 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 80 1,094 0 16 Aug 2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber Laurent Dinh Chi Jin Jascha Narain Sohl-Dickstein Samy Bengio Praneeth Netrapalli Aaron Sidford 277 3,722 0 26 May 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,364 0 22 Dec 2014
Deep Learning Face Attributes in the Wild Ziwei Liu Ping Luo Xiaogang Wang Xiaoou Tang CVBM 247 8,429 0 28 Nov 2014
Auto-Encoding Variational Bayes Diederik P. Kingma Max Welling BDL 458 16,922 0 20 Dec 2013
Fast Computation of Wasserstein Barycenters Marco Cuturi Arnaud Doucet OT 119 743 0 16 Oct 2013