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On amortizing convex conjugates for optimal transport
v1v2 (latest)

On amortizing convex conjugates for optimal transport

21 October 2022
Brandon Amos
    OT
ArXiv (abs)PDFHTMLGithub (47★)

Papers citing "On amortizing convex conjugates for optimal transport"

46 / 46 papers shown
Title
Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
Sample and Map from a Single Convex Potential: Generation using Conjugate Moment Measures
Nina Vesseron
Louis Béthune
Marco Cuturi
122
0
0
13 Mar 2025
Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction
Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction
Yanshuo Chen
Zhengmian Hu
Wei Chen
Heng Huang
OT
92
2
0
01 Nov 2024
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
Milena Gazdieva
Jaemoo Choi
Alexander Kolesov
Jaewoong Choi
Petr Mokrov
Alexander Korotin
OT
142
2
0
04 Oct 2024
On a Neural Implementation of Brenier's Polar Factorization
On a Neural Implementation of Brenier's Polar Factorization
Nina Vesseron
Marco Cuturi
93
2
0
05 Mar 2024
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Alexander Kolesov
Petr Mokrov
Igor Udovichenko
Milena Gazdieva
G. Pammer
Anastasis Kratsios
Evgeny Burnaev
Alexander Korotin
OT
89
2
0
02 Oct 2023
Computing high-dimensional optimal transport by flow neural networks
Computing high-dimensional optimal transport by flow neural networks
Chen Xu
Xiuyuan Cheng
Yao Xie
OT
119
5
0
19 May 2023
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings
Frederike Lubeck
Charlotte Bunne
Gabriele Gut
J. Castillo
L. Pelkmans
David Alvarez-Melis
OT
51
22
0
30 Sep 2022
Supervised Training of Conditional Monge Maps
Supervised Training of Conditional Monge Maps
Charlotte Bunne
Andreas Krause
Marco Cuturi
OT
131
65
0
28 Jun 2022
Meta Optimal Transport
Meta Optimal Transport
Brandon Amos
Samuel N. Cohen
Giulia Luise
I. Redko
OT
94
24
0
10 Jun 2022
Neural Optimal Transport with General Cost Functionals
Neural Optimal Transport with General Cost Functionals
Arip Asadulaev
Alexander Korotin
Vage Egiazarian
Petr Mokrov
Evgeny Burnaev
OT
100
34
0
30 May 2022
Amortized Proximal Optimization
Amortized Proximal Optimization
Juhan Bae
Paul Vicol
Jeff Z. HaoChen
Roger C. Grosse
ODL
89
14
0
28 Feb 2022
Learning Predictions for Algorithms with Predictions
Learning Predictions for Algorithms with Predictions
M. Khodak
Maria-Florina Balcan
Ameet Talwalkar
Sergei Vassilvitskii
73
27
0
18 Feb 2022
Generative Modeling with Optimal Transport Maps
Generative Modeling with Optimal Transport Maps
Litu Rout
Alexander Korotin
Evgeny Burnaev
OTDiffM
180
70
0
06 Oct 2021
Implicit Riemannian Concave Potential Maps
Implicit Riemannian Concave Potential Maps
Danilo Jimenez Rezende
S. Racanière
AI4CE
88
7
0
04 Oct 2021
Faster Matchings via Learned Duals
Faster Matchings via Learned Duals
M. Dinitz
Sungjin Im
Thomas Lavastida
Benjamin Moseley
Sergei Vassilvitskii
49
69
0
20 Jul 2021
Riemannian Convex Potential Maps
Riemannian Convex Potential Maps
Samuel N. Cohen
Brandon Amos
Y. Lipman
63
22
0
18 Jun 2021
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2
  Benchmark
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
143
89
0
03 Jun 2021
Optimizing Functionals on the Space of Probabilities with Input Convex
  Neural Networks
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
Large-Scale Wasserstein Gradient Flows
Petr Mokrov
Alexander Korotin
Lingxiao Li
Aude Genevay
Justin Solomon
Evgeny Burnaev
72
75
0
01 Jun 2021
Learning to Optimize: A Primer and A Benchmark
Learning to Optimize: A Primer and A Benchmark
Tianlong Chen
Xiaohan Chen
Wuyang Chen
Howard Heaton
Jialin Liu
Zhangyang Wang
W. Yin
243
235
0
23 Mar 2021
Continuous Wasserstein-2 Barycenter Estimation without Minimax
  Optimization
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Alexander Korotin
Lingxiao Li
Justin Solomon
Evgeny Burnaev
127
53
0
02 Feb 2021
Convex Potential Flows: Universal Probability Distributions with Optimal
  Transport and Convex Optimization
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Chin-Wei Huang
Ricky T. Q. Chen
Christos Tsirigotis
Aaron Courville
OT
177
98
0
10 Dec 2020
Continuous Regularized Wasserstein Barycenters
Continuous Regularized Wasserstein Barycenters
Lingxiao Li
Aude Genevay
Mikhail Yurochkin
Justin Solomon
65
47
0
28 Aug 2020
Scalable Computations of Wasserstein Barycenter via Input Convex Neural
  Networks
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
JiaoJiao Fan
Amirhossein Taghvaei
Yongxin Chen
67
59
0
08 Jul 2020
Multi-marginal Wasserstein GAN
Multi-marginal Wasserstein GAN
Jingyun Liang
Langyuan Mo
Yifan Zhang
Kui Jia
Chunhua Shen
Mingkui Tan
52
78
0
03 Nov 2019
Wasserstein-2 Generative Networks
Wasserstein-2 Generative Networks
Alexander Korotin
Vage Egiazarian
Arip Asadulaev
Alexander Safin
Evgeny Burnaev
GAN
196
108
0
28 Sep 2019
Optimal transport mapping via input convex neural networks
Optimal transport mapping via input convex neural networks
Ashok Vardhan Makkuva
Amirhossein Taghvaei
Sewoong Oh
Jason D. Lee
OT
52
201
0
28 Aug 2019
Adversarial Computation of Optimal Transport Maps
Adversarial Computation of Optimal Transport Maps
Jacob Leygonie
Jennifer She
Amjad Almahairi
Sai Rajeswar
Aaron Courville
GANOT
47
21
0
24 Jun 2019
On Scalable and Efficient Computation of Large Scale Optimal Transport
On Scalable and Efficient Computation of Large Scale Optimal Transport
Yujia Xie
Minshuo Chen
Haoming Jiang
T. Zhao
H. Zha
OT
64
44
0
01 May 2019
2-Wasserstein Approximation via Restricted Convex Potentials with
  Application to Improved Training for GANs
2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs
Amirhossein Taghvaei
Amin Jalali
63
43
0
19 Feb 2019
Glow: Generative Flow with Invertible 1x1 Convolutions
Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma
Prafulla Dhariwal
BDLDRL
300
3,138
0
09 Jul 2018
Wasserstein Divergence for GANs
Wasserstein Divergence for GANs
Jiqing Wu
Zhiwu Huang
Janine Thoma
Dinesh Acharya
Luc Van Gool
75
139
0
04 Dec 2017
Large-Scale Optimal Transport and Mapping Estimation
Large-Scale Optimal Transport and Mapping Estimation
Vivien Seguy
B. Damodaran
Rémi Flamary
Nicolas Courty
Antoine Rolet
Mathieu Blondel
OT
68
249
0
07 Nov 2017
On the regularization of Wasserstein GANs
On the regularization of Wasserstein GANs
Henning Petzka
Asja Fischer
Denis Lukovnikov
GAN
44
212
0
26 Sep 2017
Continuously Differentiable Exponential Linear Units
Continuously Differentiable Exponential Linear Units
Jonathan T. Barron
56
141
0
24 Apr 2017
Input Convex Neural Networks
Input Convex Neural Networks
Brandon Amos
Lei Xu
J. Zico Kolter
280
623
0
22 Sep 2016
Stochastic Optimization for Large-scale Optimal Transport
Stochastic Optimization for Large-scale Optimal Transport
Aude Genevay
Marco Cuturi
Gabriel Peyré
Francis R. Bach
OT
75
468
0
27 May 2016
A Multi-Batch L-BFGS Method for Machine Learning
A Multi-Batch L-BFGS Method for Machine Learning
A. Berahas
J. Nocedal
Martin Takáč
ODL
67
112
0
19 May 2016
Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
354
10,192
0
16 Mar 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units
  (ELUs)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert
Thomas Unterthiner
Sepp Hochreiter
300
5,532
0
23 Nov 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg3DV
1.9K
77,341
0
18 May 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
463
43,328
0
11 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.0K
150,260
0
22 Dec 2014
Deep Learning Face Attributes in the Wild
Deep Learning Face Attributes in the Wild
Ziwei Liu
Ping Luo
Xiaogang Wang
Xiaoou Tang
CVBM
244
8,424
0
28 Nov 2014
Identifying and attacking the saddle point problem in high-dimensional
  non-convex optimization
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N. Dauphin
Razvan Pascanu
Çağlar Gülçehre
Kyunghyun Cho
Surya Ganguli
Yoshua Bengio
ODL
129
1,389
0
10 Jun 2014
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation
  Distances
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
Marco Cuturi
OT
218
4,277
0
04 Jun 2013
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