Florent Gérard Krzakala

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Publications associées (66)

The committee machine: computational to statistical gaps in learning a two-layers neural network

Nicolas Macris, Jean François Emmanuel Barbier, Florent Gérard Krzakala, Lenka Zdeborová

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 scenario in multi-layer neural networks. In this paper, we provide a rigor ...

IOP PUBLISHING LTD2019

Entropy and mutual information in models of deep neural networks

Nicolas Macris, Jean François Emmanuel Barbier, Clément Dominique Luneau, Florent Gérard Krzakala, Lenka Zdeborová

We examine a class of stochastic deep learning models with a tractable method to compute information-theoretic quantities. Our contributions are three-fold: (i) we show how entropies and mutual informations can be derived from heuristic statistical physics ...

2019

The committee machine: Computational to statistical gaps in learning a two-layers neural network

Nicolas Macris, Jean François Emmanuel Barbier, Florent Gérard Krzakala, Lenka Zdeborová

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

The Mutual Information in Random Linear Estimation Beyond i.i.d. Matrices

Nicolas Macris, Jean François Emmanuel Barbier, Florent Gérard Krzakala

There has been definite progress recently in proving the variational single-letter formula given by the heuristic replica method for various estimation problems. In particular, the replica formula for the mutual information in the case of noisy linear esti ...

IEEE2018

Entropy and mutual information in models of deep neural networks

Nicolas Macris, Jean François Emmanuel Barbier, Clément Dominique Luneau, Florent Gérard Krzakala, Lenka Zdeborová

We examine a class of stochastic deep learning models with a tractable method to compute information-theoretic quantities. Our contributions are three-fold: (i) We show how entropies and mutual informations can be derived from heuristic statistical physics ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Scampi: a robust approximate message-passing framework for compressive imaging

Jean François Emmanuel Barbier, Florent Gérard Krzakala

Reconstruction of images from noisy linear measurements is a core problem in image processing, for which convex optimization methods based on total variation (TV) minimization have been the long-standing state-of-the-art. We present an alternative probabil ...

Iop Publishing Ltd2016

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