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Le Séminaire Palaisien

Le Séminaire Palaisien | Les statistiques et l'apprentissage machine

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Centre Inria-Saclay - Bâtiment Alan Turing

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Le séminaire Palaisien réunit, chaque premier mardi du mois, la vaste communauté de recherche de Saclay autour de la statistique et de l'apprentissage machine.
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Chaque session de séminaire est divisée en 2 présentations scientifiques de 40 minutes chacune : 30 minutes d’exposé et 10 minutes de questions, suivies par un goûter.

Pierre Ablin et François-Pierre Paty, deux doctorants à Saclay, animeront la session du 5 novembre.

 

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« Deep learning for inverse problems: solving the Lasso with neural networks » - Pierre Ablin
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Deep learning architectures are becoming ubiquitous for inverse problem resolution. We start by reviewing one of the earliest examples. It consists in using recurrent neural networks to solve the Lasso problem: one of the most popular algorithm to solve the Lasso, the Iterative Shrinkage-Thresholding Algorithm (ISTA), iterates matrix multiplications and non-linearities until convergence. Therefore, T iterations of ISTA are equivalent to a T layers neural network. The weights of the neural network can then be learned to accelerate resolution of the Lasso. 
In a second step, we study the weights that are learned by such networks. In particular, we show that the last layers of such deep networks can only learn a better step-size for the ISTA iterations.

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« Regularizing Optimal Transport Using Regularity Theory » - François-Pierre Paty
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Optimal transport (OT) suffers from the curse of dimensionality. In this talk, I will present a new regularization of OT leveraging regularity of the Brenier map. Instead of considering regularity as a a property that can be proved under suitable assumptions, we consider regularity as a condition that must be enforced when estimating OT. From an algorithmic point of view, this leads to an infinite-dimensional optimization problem, which, when dealing with discrete measures, can be rewritten as a finite-dimensional separately convex problem. From a statistical point of view, this defines new estimators of the OT map and 2-Wasserstein distance between arbitrary measures, for which I will show numerical evidence of their performance.

(Based on a joint work with Alexandre d'Aspremont and Marco Cuturi)

 

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Le séminaire aura lieu le 5 novembre 2019 de 16h00 à 17h30 au centre Inria-Saclay - bâtiment Alan Turing - Amphithéâtre Sophie Germain.

Inscriptions gratuites mais obligatoires dans la limite des places disponibles.
Pour des raisons de sécurité, toute personne non-inscrite ne pourra accéder au lieu du séminaire