Recherche

Afficher tous les résultats pour

Accueil

Personne

Nicolas Macris

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (65)

Universal Sparse Superposition Codes With Spatial Coupling and GAMP Decoding

Nicolas Macris, Mohamad Baker Dia, Jean François Emmanuel Barbier

Sparse superposition codes, or sparse regression codes, constitute a new class of codes, which was first introduced for communication over the additive white Gaussian noise (AWGN) channel. It has been shown that such codes are capacity-achieving over the A ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Efficient Quantum Algorithms for GHZ and W States, and Implementation on the IBM Quantum Computer

Marc-André Dupertuis, Nicolas Macris, Clément Christian Javerzac-Galy, Kenichi Komagata, Fabien Gremion, Chun Lam Chan, Jarla Thiesbrummel, Romain Fournier

Efficient deterministic algorithms are proposed with logarithmic step complexities for the generation of entangled GHZ(N) and W-N states useful for quantum networks, and an implementation on the IBM quantum computer up to N = 16 is demonstrated. Improved q ...

WILEY2019

Mutual Information for Low-Rank Even-Order Symmetric Tensor Factorization

Nicolas Macris, Jean François Emmanuel Barbier, Clément Dominique Luneau

We consider a statistical model for finite-rank symmetric tensor factorization and prove a single-letter variational expression for its mutual information when the tensor is of even order. The proof uses the adaptive interpolation method, for which rank-on ...

IEEE2019

Optimal errors and phase transitions in high-dimensional generalized linear models

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

Generalized linear models (GLMs) are used in high-dimensional machine learning, statistics, communications, and signal processing. In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-corre ...

National Academy of Sciences2019

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

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 contribution, we provide ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

Adaptive Path Interpolation for Sparse Systems: Application to a Simple Censored Block Model

Nicolas Macris, Chun Lam Chan, Jean François Emmanuel Barbier

A new adaptive path interpolation method has been recently developed as a simple and versatile scheme to calculate exactly the asymptotic mutual information of Bayesian inference problems defined on dense factor graphs. These include random linear and gene ...

IEEE2018

The Velocity of the Propagating Wave for Spatially Coupled Systems With Applications to LDPC Codes

Nicolas Macris, Rafah El-Khatib

We consider the dynamics of message passing for spatially coupled codes and, in particular, the set of density evolution equations that tracks the profile of decoding errors along the spatial direction of coupling. It is known that, for suitable boundary c ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

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

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

Spatial Coupling as a Proof Technique and Three Applications

Nicolas Macris, Rüdiger Urbanke, 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

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

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

Nicolas Macris, Marc Vuffray, Vahid Aref

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

Analysis of Coupled Scalar Systems by Displacement Convexity

Nicolas Macris, Rüdiger Urbanke, 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

Threshold Saturation for Spatially Coupled LDPC and LDGM Codes on BMS Channels

Nicolas Macris, Henry Pfister

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

Approaching the Rate-Distortion Limit by spatial Coupling with Belief Propagation and Decimation

Nicolas Macris, Marc Vuffray, Vahid Aref

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

Threshold Saturation in Spatially Coupled Constraint Satisfaction Problems

Nicolas Macris, Rüdiger Urbanke, Seyed Hamed Hassani

We consider chains of random constraint satisfaction models that are spatially coupled across a finite window along the chain direction. We investigate their phase diagram at zero temperature using the survey propagation formalism and the interpolation met ...

Springer Verlag2013

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

Nicolas Macris, Rüdiger Urbanke, 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

Chains of mean field models

Nicolas Macris, Rüdiger Urbanke, Seyed Hamed Hassani

2012

Beyond the Bethe Free Energy of LDPC Codes via Polymer Expansions

Nicolas Macris, Marc Vuffray

The loop series provides a formal way to write down corrections to the Bethe entropy (and/or free energy) of graphical models. We provide methods to rigorously control such expansions for low-density parity-check codes used over a highly noisy binary symme ...

Ieee2012

Page 2 sur 4