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Learning Generative Models with Sinkhorn Divergences
v1v2v3 (latest)

Learning Generative Models with Sinkhorn Divergences

International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
1 June 2017
Aude Genevay
Gabriel Peyré
Marco Cuturi
    OT
ArXiv (abs)PDFHTML

Papers citing "Learning Generative Models with Sinkhorn Divergences"

49 / 399 papers shown
Minimax estimation of smooth optimal transport maps
Minimax estimation of smooth optimal transport maps
Jan-Christian Hütter
Philippe Rigollet
OT
241
29
0
14 May 2019
Learning Generative Models across Incomparable Spaces
Learning Generative Models across Incomparable SpacesInternational Conference on Machine Learning (ICML), 2019
Charlotte Bunne
David Alvarez-Melis
Andreas Krause
Stefanie Jegelka
GAN
161
118
0
14 May 2019
Learning Embeddings into Entropic Wasserstein Spaces
Learning Embeddings into Entropic Wasserstein SpacesInternational Conference on Learning Representations (ICLR), 2019
Charlie Frogner
F. Mirzazadeh
Justin Solomon
154
34
0
08 May 2019
Sliced Wasserstein Generative Models
Jiqing Wu
Zhiwu Huang
Dinesh Acharya
Wen Li
Janine Thoma
D. Paudel
Luc Van Gool
DiffM
329
136
0
10 Apr 2019
Wasserstein Adversarial Regularization (WAR) on label noise
Wasserstein Adversarial Regularization (WAR) on label noise
Kilian Fatras
B. Bushan
Sylvain Lobry
Rémi Flamary
D. Tuia
Nicolas Courty
224
29
0
08 Apr 2019
The Born Supremacy: Quantum Advantage and Training of an Ising Born
  Machine
The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
Brian Coyle
Daniel Mills
V. Danos
E. Kashefi
458
172
0
03 Apr 2019
Feature Intertwiner for Object Detection
Feature Intertwiner for Object Detection
Hongyang Li
Bo Dai
Shaoshuai Shi
Wanli Ouyang
Xiaogang Wang
OOD
129
15
0
28 Mar 2019
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task
  Supervision at Test-Time
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time
Gabriel Huang
Hugo Larochelle
Damien Scieur
SSLOOD
286
22
0
22 Feb 2019
Sinkhorn Divergence of Topological Signature Estimates for Time Series
  Classification
Sinkhorn Divergence of Topological Signature Estimates for Time Series Classification
C. Stephen
57
0
0
14 Feb 2019
Wasserstein Barycenter Model Ensembling
Wasserstein Barycenter Model EnsemblingInternational Conference on Learning Representations (ICLR), 2019
Pierre Dognin
Igor Melnyk
Youssef Mroueh
Jerret Ross
Cicero Nogueira dos Santos
Tom Sercu
206
27
0
13 Feb 2019
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
(q,p)-Wasserstein GANs: Comparing Ground Metrics for Wasserstein GANs
Anton Mallasto
J. Frellsen
Wouter Boomsma
Aasa Feragen
173
15
0
10 Feb 2019
Minimax estimation of smooth densities in Wasserstein distance
Minimax estimation of smooth densities in Wasserstein distance
Jonathan Niles-Weed
Quentin Berthet
OT
216
45
0
05 Feb 2019
AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz
  Continuous Penalty in Deep Convolutional Neural Networks
AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks
J. Lyu
Shuai Zhang
Y. Qi
Jack Xin
154
30
0
24 Jan 2019
Improving Sequence-to-Sequence Learning via Optimal Transport
Improving Sequence-to-Sequence Learning via Optimal Transport
Liqun Chen
Yizhe Zhang
Ruiyi Zhang
Chenyang Tao
Zhe Gan
Haichao Zhang
Bai Li
Dinghan Shen
Changyou Chen
Lawrence Carin
OT
192
95
0
18 Jan 2019
Asymptotic distribution and convergence rates of stochastic algorithms
  for entropic optimal transportation between probability measures
Asymptotic distribution and convergence rates of stochastic algorithms for entropic optimal transportation between probability measures
Bernard Bercu
Jérémie Bigot
342
25
0
21 Dec 2018
Massively scalable Sinkhorn distances via the Nyström method
Massively scalable Sinkhorn distances via the Nyström method
Jason M. Altschuler
Francis R. Bach
Alessandro Rudi
Jonathan Niles-Weed
251
118
0
12 Dec 2018
Stochastic Deep Networks
Stochastic Deep NetworksInternational Conference on Machine Learning (ICML), 2018
Gwendoline de Bie
Gabriel Peyré
Marco Cuturi
254
24
0
19 Nov 2018
Sorting out Lipschitz function approximation
Sorting out Lipschitz function approximation
Cem Anil
James Lucas
Roger C. Grosse
364
351
0
13 Nov 2018
Empirical Regularized Optimal Transport: Statistical Theory and
  Applications
Empirical Regularized Optimal Transport: Statistical Theory and Applications
M. Klatt
Carla Tameling
Axel Munk
OT
248
65
0
23 Oct 2018
Interpolating between Optimal Transport and MMD using Sinkhorn
  Divergences
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences
Jean Feydy
Thibault Séjourné
François-Xavier Vialard
S. Amari
A. Trouvé
Gabriel Peyré
OT
324
599
0
18 Oct 2018
Point Cloud GAN
Point Cloud GAN
Chun-Liang Li
Manzil Zaheer
Yang Zhang
Barnabás Póczós
Ruslan Salakhutdinov
3DPC
220
226
0
13 Oct 2018
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample
  Likelihoods in GANs
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Yogesh Balaji
Hamed Hassani
Rama Chellappa
Soheil Feizi
GANDRL
247
22
0
09 Oct 2018
Sample Complexity of Sinkhorn divergences
Sample Complexity of Sinkhorn divergences
Aude Genevay
Lénaïc Chizat
Francis R. Bach
Marco Cuturi
Gabriel Peyré
OT
367
322
0
05 Oct 2018
Sinkhorn AutoEncoders
Sinkhorn AutoEncoders
Giorgio Patrini
Rianne van den Berg
Patrick Forré
M. Carioni
Samarth Bhargav
Max Welling
Tim Genewein
Frank Nielsen
DiffM
248
0
0
02 Oct 2018
Entropic optimal transport is maximum-likelihood deconvolution
Entropic optimal transport is maximum-likelihood deconvolution
Philippe Rigollet
Jonathan Niles-Weed
OT
226
82
0
14 Sep 2018
Second-order Democratic Aggregation
Second-order Democratic Aggregation
Tsung-Yu Lin
Subhransu Maji
Piotr Koniusz
112
31
0
22 Aug 2018
Neural Network Encapsulation
Neural Network Encapsulation
Hongyang Li
Xiaoyang Guo
Bo Dai
Wanli Ouyang
Xiaogang Wang
144
54
0
11 Aug 2018
Towards Optimal Transport with Global Invariances
Towards Optimal Transport with Global Invariances
David Alvarez-Melis
Stefanie Jegelka
Tommi Jaakkola
OT
189
78
0
25 Jun 2018
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal
  Transport and Diffusions
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions
Antoine Liutkus
Umut Simsekli
Szymon Majewski
Alain Durmus
Fabian-Robert Stöter
DiffM
321
126
0
21 Jun 2018
Differential Properties of Sinkhorn Approximation for Learning with
  Wasserstein Distance
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Giulia Luise
Alessandro Rudi
Massimiliano Pontil
C. Ciliberto
OT
175
139
0
30 May 2018
On gradient regularizers for MMD GANs
On gradient regularizers for MMD GANs
Michael Arbel
Danica J. Sutherland
Mikolaj Binkowski
Arthur Gretton
344
101
0
29 May 2018
Wasserstein Variational Inference
Wasserstein Variational Inference
Luca Ambrogioni
Umut Güçlü
Yağmur Güçlütürk
Max Hinne
E. Maris
Marcel van Gerven
BDLDRL
206
42
0
29 May 2018
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
Unsupervised Alignment of Embeddings with Wasserstein Procrustes
Edouard Grave
Armand Joulin
Quentin Berthet
295
212
0
29 May 2018
Optimal Transport for structured data with application on graphs
Optimal Transport for structured data with application on graphs
Titouan Vayer
Laetitia Chapel
Rémi Flamary
R. Tavenard
Nicolas Courty
OT
262
311
0
23 May 2018
Wasserstein Measure Coresets
Wasserstein Measure Coresets
Sebastian Claici
Aude Genevay
Justin Solomon
159
14
0
18 May 2018
Generative Adversarial Networks (GANs): What it can generate and What it
  cannot?
Generative Adversarial Networks (GANs): What it can generate and What it cannot?
P Manisha
Sujit Gujar
GAN
111
0
0
31 Mar 2018
Improving GANs Using Optimal Transport
Improving GANs Using Optimal TransportInternational Conference on Learning Representations (ICLR), 2018
Tim Salimans
Han Zhang
Alec Radford
Dimitris N. Metaxas
OTGAN
295
335
0
15 Mar 2018
Computational Optimal Transport
Computational Optimal Transport
Gabriel Peyré
Marco Cuturi
OT
1.4K
2,415
0
01 Mar 2018
Distance Measure Machines
Distance Measure Machines
A. Rakotomamonjy
Abraham Traoré
Maxime Bérar
Rémi Flamary
Nicolas Courty
202
12
0
01 Mar 2018
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Gonzalo E. Mena
David Belanger
Scott W. Linderman
Jasper Snoek
258
302
0
23 Feb 2018
On the Convergence and Robustness of Training GANs with Regularized
  Optimal Transport
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
Maziar Sanjabi
Jimmy Ba
Meisam Razaviyayn
Jason D. Lee
GAN
230
144
0
22 Feb 2018
Learning to Match via Inverse Optimal Transport
Learning to Match via Inverse Optimal Transport
Ruilin Li
X. Ye
Haomin Zhou
H. Zha
FedML
308
54
0
10 Feb 2018
Innovative Non-parametric Texture Synthesis via Patch Permutations
Innovative Non-parametric Texture Synthesis via Patch Permutations
Ryan Webster
80
4
0
14 Jan 2018
Demystifying MMD GANs
Demystifying MMD GANs
Mikolaj Binkowski
Danica J. Sutherland
Michael Arbel
Arthur Gretton
EGVM
2.2K
1,798
0
04 Jan 2018
Sobolev GAN
Sobolev GAN
Youssef Mroueh
Chun-Liang Li
Tom Sercu
Anant Raj
Yu Cheng
131
118
0
14 Nov 2017
A unified framework for hard and soft clustering with regularized
  optimal transport
A unified framework for hard and soft clustering with regularized optimal transport
Jean-Frédéric Diebold
Nicolas Papadakis
Arnaud Dessein
Charles-Alban Deledalle
FedML
258
9
0
12 Nov 2017
Parametric Adversarial Divergences are Good Losses for Generative
  Modeling
Parametric Adversarial Divergences are Good Losses for Generative Modeling
Gabriel Huang
Hugo Berard
Ahmed Touati
Gauthier Gidel
Pascal Vincent
Damien Scieur
GAN
237
1
0
08 Aug 2017
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised
  non-linear dictionary learning
Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning
M. Schmitz
Matthieu Heitz
Nicolas Bonneel
Fred-Maurice Ngole-Mboula
D. Coeurjolly
Marco Cuturi
Gabriel Peyré
Jean-Luc Starck
OT
285
141
0
07 Aug 2017
Semi-discrete optimal transport - the case p=1
Semi-discrete optimal transport - the case p=1
Valentin N. Hartmann
Dominic Schuhmacher
OT
117
9
0
23 Jun 2017
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