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1706.00292
Cited By
Learning Generative Models with Sinkhorn Divergences
1 June 2017
Aude Genevay
Gabriel Peyré
Marco Cuturi
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Papers citing
"Learning Generative Models with Sinkhorn Divergences"
50 / 375 papers shown
Title
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Sparsistency for Inverse Optimal Transport
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Clarice Poon
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Tara Akhound-Sadegh
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Alexander Tong
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A Unified View of Differentially Private Deep Generative Modeling
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Raouf Kerkouche
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Topological Node2vec: Enhanced Graph Embedding via Persistent Homology
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Match-And-Deform: Time Series Domain Adaptation through Optimal Transport and Temporal Alignment
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Laetitia Chapel
Nicolas Courty
Chloé Friguet
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Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
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Rémi Flamary
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Differentiable Transportation Pruning
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Jan van Gemert
Torsten Hoefler
Bert Moons
E. Eleftheriou
Bram-Ernst Verhoef
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Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients
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Allan D. Jepson
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Simulation-free Schrödinger bridges via score and flow matching
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Lazar Atanackovic
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Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
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Unbalanced Optimal Transport: A Unified Framework for Object Detection
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Pierre-François De Plaen
Johan A. K. Suykens
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Tinne Tuytelaars
Luc Van Gool
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SoK: Privacy-Preserving Data Synthesis
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On a Relation Between the Rate-Distortion Function and Optimal Transport
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Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
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Importance Sparsification for Sinkhorn Algorithm
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Jun Yu
Tao Li
Cheng Meng
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Effective Neural Topic Modeling with Embedding Clustering Regularization
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Xinshuai Dong
Thong Nguyen
A. Luu
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Coupled Variational Autoencoder
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05 Jun 2023
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
Jacob Lindbäck
Zesen Wang
Mikael Johansson
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40
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Feature-aligned N-BEATS with Sinkhorn divergence
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Myung-joo Kang
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Weak Limits for Empirical Entropic Optimal Transport: Beyond Smooth Costs
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Shayan Hundrieser
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Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence
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10 May 2023
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
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V. Rolland
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Feiyang Kang
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Yi Zeng
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Multisample Flow Matching: Straightening Flows with Minibatch Couplings
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Heli Ben-Hamu
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Brandon Amos
Y. Lipman
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Nearest-Neighbor Sampling Based Conditional Independence Testing
Shuai Li
Ziqi Chen
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Keypoint-Guided Optimal Transport
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Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport
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Light Unbalanced Optimal Transport
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An Approximation Theory Framework for Measure-Transport Sampling Algorithms
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Transformed Distribution Matching for Missing Value Imputation
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Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps
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Michal Klein
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Domain Adaptation for Time Series Under Feature and Label Shifts
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Improving and generalizing flow-based generative models with minibatch optimal transport
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Self-Consistent Velocity Matching of Probability Flows
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Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
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