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Analyse en Composantes Principales (ACP)

Analyse en Composantes Principales (ACP)

ciser la signification statistique des résultats obtenus. L’ACP est illustrée dans ce chapitre à travers l’étude de données élémentaires. Elles sont constituées des moyennes sur dix ans des températures moyennes mensuelles de 32 villes françaises. La matrice initiale X est donc (32 12). Les colonnes sont l’ob- servation à différents instants d’une même variable; elles son

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arXiv:1812.09066v3 [cs.LG] 1 Jul 2019

arXiv:1812.09066v3 [cs.LG] 1 Jul 2019

4Laboratoire de Physique Statistique, CNRS & Universit e Pierre & Marie Curie & Ecole Normale Sup erieure & PSL Universit e, 75005 Paris, France (Dated: July 2, 2019) Gradient-descent-based algorithms and their stochastic versions have widespread applications in machine learning and statistical inference. In this work we perform an analytic study of the per-formances of the one most commonly ...

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The committee machine: Computational to statistical gaps ...

The committee machine: Computational to statistical gaps ...

The committee machine: Computational to statistical gaps in learning a two-layers neural network Benjamin Aubin? y, Antoine Maillard , Jean Barbier Florent Krzakalay, Nicolas Macris , Lenka Zdeborová? Abstract Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student ...

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Pressions et effets cumulés produits par les usages de loisir

Pressions et effets cumulés produits par les usages de loisir

Modélisation par apprentissage statistique (machine learning) Données de comptage « Life+ Pêche à Pied » (2014-2016) Variables explicatives (n=16) Evénements sportifs: AOT et DA des DDTM 62, 80 et 76 (2015-2016) Forêts aléatoires (Breiman 2011). •380 événements •110 cartes géoréférencées ENCADRÉES OUI NON. Résultats. Manifestations sportives https://is.gd/PKuFpw ...

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Approximate Survey Propagation for Statistical Inference

Approximate Survey Propagation for Statistical Inference

Approximate Survey Propagation for Statistical Inference Fabrizio Antenucci,1,2 Florent Krzakala,3 Pierfrancesco Urbani,1 and Lenka Zdeborov a1 1 Institut de physique th eorique, Universit e Paris Saclay, CNRS, CEA, F-91191 Gif-sur-Yvette, France 2Soft and Living Matter Lab., Rome Unit of CNR-NANOTEC, Institute of Nanotechnology, Piazzale Aldo Moro 5, I-00185, Rome, Italy

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Approximate Message-Passing for Convex Optimization with ...

Approximate Message-Passing for Convex Optimization with ...

Approximate Message-Passing for Convex Optimization with Non-Separable Penalties Andre Manoely;z, Florent Krzakala], Bertrand Thiriony, Gael Varoquaux¨ yand Lenka Zdeborova´z yParietal Team, Inria, Neurospin, CEA, zInstitut de Physique Theorique, CEA and´]Laboratoire de Physique Statistique, ENS Convex optimization with non-separable penalties. ...

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export.arxiv.org

export.arxiv.org

2.1 Basic underlying model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 AMP algorithm and state evolution ...

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export.arxiv.org

export.arxiv.org

Marvels and pitfalls of the Langevin algorithm in noisy high-dimensional inference Stefano Sarao Mannelli,1 Giulio Biroli,2 Chiara Cammarota,3 Florent Krzakala,4 Pierfrancesco Urb

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Entropy and mutual information in models of deep neural ...

Entropy and mutual information in models of deep neural ...

1Laboratoire de Physique Statistique, École Normale Supérieure, PSL University 2 ... statistics [15] and machine learning problems [16, 17]. In particular, we use advanced mean field theory [18] and the heuristic replica method [10, 6], along with its recent extension to multi-layer estimation [7, 8], in order to derive the above formula (3). This derivation is lengthy and thus given in the ...

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Approximate message passing with restricted Boltzmann ...

Approximate message passing with restricted Boltzmann ...

Belief propagation and replicas for inference and learning in a kinetic Ising model with hidden spins C Battistin, J Hertz, J Tyrcha et al. Approximate message passing with restricted Boltzmann machine priors View the table of contents for this issue, or go to the journal homepage for more Home Search Collections Journals About Contact us My IOPscience. J. Stat. Mech. (2016) 073401 Approximate ...

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The committee machine: Computational to statistical gaps ...

The committee machine: Computational to statistical gaps ...

The committee machine: Computational to statistical gaps in learning a two-layers neural network Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová To cite this version: Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, et al.. The committee machine: Computational to statistical gaps in learning a two-layers neural ...

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