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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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A Unified View of Differentially Private Deep Generative Modeling
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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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Nicolas Courty
Chloé Friguet
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Bram-Ernst Verhoef
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Simulation-free Schrödinger bridges via score and flow matching
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Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
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Anit Kumar Sahu
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Unbalanced Optimal Transport: A Unified Framework for Object Detection
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Recent Advances in Optimal Transport for Machine Learning
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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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Effective Neural Topic Modeling with Embedding Clustering Regularization
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Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
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Zesen Wang
Mikael Johansson
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35
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Feature-aligned N-BEATS with Sinkhorn divergence
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Speeding up Monte Carlo Integration: Control Neighbors for Optimal Convergence
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Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
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Heli Ben-Hamu
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Brandon Amos
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Nearest-Neighbor Sampling Based Conditional Independence Testing
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Keypoint-Guided Optimal Transport
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Light Unbalanced Optimal Transport
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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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Domain Adaptation for Time Series Under Feature and Label Shifts
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