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Nicolas Macris

Related publications (73)

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Entropy and mutual information in models of deep neural networks

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

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

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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

The Bethe Free Energy Allows to Compute the Conditional Entropy of Graphical Code Instances: A Proof From the Polymer Expansion

Nicolas Macris, Marc Vuffray

The main objective of this paper is to explore the precise relationship between the Bethe free energy (or entropy) and the Shannon conditional entropy of graphical error correcting codes. The main result shows that the Bethe free energy associated with a l ...

Ieee-Inst Electrical Electronics Engineers Inc2016

The Velocity of the Propagating Wave for General Coupled Scalar Systems

Nicolas Macris, Rafah El-Khatib

We consider spatially coupled systems governed by a set of scalar density evolution equations. Such equations track the behavior of message-passing algorithms used, for example, in coding, sparse sensing, or constraint-satisfaction problems. Assuming that ...

Ieee2016

Spatial Coupling as a Proof Technique and Three Applications

Rüdiger Urbanke, Nicolas Macris, Andrei Giurgiu

The aim of this paper is to show that spatial coupling can be viewed not only as a means to build better graphical models, but also as a tool to better understand uncoupled models. The starting point is the observation that some asymptotic properties of gr ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Approaching the Rate-Distortion Limit With Spatial Coupling, Belief Propagation, and Decimation

Nicolas Macris, Vahid Aref, Marc Vuffray

We investigate an encoding scheme for lossy compression of a binary symmetric source based on simple spatially coupled low-density generator-matrix codes. The degree of the check nodes is regular and the one of code-bits is Poisson distributed with an aver ...

Institute of Electrical and Electronics Engineers2015

Spatially-coupled low-density parity-check (LDPC) codes, which were first introduced as LDPC convolutional codes, have been shown to exhibit excellent performance under low-complexity belief-propagation decoding. This phenomenon is now termed threshold sat ...

Institute of Electrical and Electronics Engineers2014

Analysis of Coupled Scalar Systems by Displacement Convexity

Rüdiger Urbanke, Nicolas Macris, Rafah El-Khatib

Potential functionals have been introduced recently as an important tool for the analysis of coupled scalar systems (e.g. density evolution equations). In this contribution we investigate interesting properties of this potential. Using the tool of displace ...

Ieee2014

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We investigate an encoding scheme for lossy compression based on spatially coupled Low-Density Generator-Matrix codes. The degree distributions are regular, or are Poisson on the code-bit side and check-regular which allows use for any compression rate. Th ...

2013

Displacement Convexity - A Useful Framework for the Study of Spatially Coupled Codes

Rüdiger Urbanke, Nicolas Macris, Rafah El-Khatib

Spatial coupling has recently emerged as a powerful paradigm to construct graphical models that work well under low-complexity message-passing algorithms. Although much progress has been made on the analysis of spatially coupled models under message passin ...

Ieee2013