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Conference papers

Learning with minibatch Wasserstein : asymptotic and gradient properties

Kilian Fatras 1, 2 Younes Zine 2 Rémi Flamary 3, 4 Rémi Gribonval 5 Nicolas Courty 1
1 OBELIX - Observation de l’environnement par imagerie complexe
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, Inria Rennes – Bretagne Atlantique
5 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
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Submitted on : Monday, March 9, 2020 - 11:23:46 AM
Last modification on : Tuesday, May 17, 2022 - 2:34:26 PM
Long-term archiving on: : Wednesday, June 10, 2020 - 1:43:39 PM

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  • HAL Id : hal-02502329, version 1
  • ARXIV : 1910.04091

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Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval, Nicolas Courty. Learning with minibatch Wasserstein : asymptotic and gradient properties. AISTATS 2020 - 23nd International Conference on Artificial Intelligence and Statistics, Jun 2020, Palermo, Italy. pp.1-20. ⟨hal-02502329⟩

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