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

Publications associées (40)

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Sharp Bounds for Optimal Decoding of Low-Density Parity-Check Codes

Nicolas Macris, Shrinivas Kudekar

Consider communication over a binary-input memoryless output-symmetric channel with low-density parity-check (LDPC) codes and maximum a posteriori (MAP) decoding. The replica method of spin glass theory allows to conjecture an analytic formula for the aver ...

2009

Mutual Information based Channel Selection for Speaker Diarization of Meetings Data

Hervé Bourlard, Fabio Valente, Deepu Vijayasenan

In the meeting case scenario, audio is often recorded using Multiple Distance Microphones (MDM) in a non-intrusive manner. Typically a beamforming is performed in order to obtain a single enhanced signal out of the multiple channels. This paper investigate ...

2009

Dictionary Learning Based Dimensionality Reduction for Classification

Pierre Vandergheynst, Karin Schnass

In this article we present a signal model for classification based on a low dimensional dictionary embedded into the high dimensional signal space. We develop an alternate projection algorithm to find the embedding and the dictionary and finally test the c ...

2008

Locally Testable Codes

Mahdi Cheraghchi Bashi Astaneh

Error correcting codes are combinatorial objects that allow reliable recovery of information in presence of errors by cleverly augmenting the original information with a certain amount of redundancy. The availability of efficient means of error detection i ...

2005

Variational Information Maximization for Population Coding

The goal of neural processing assemblies is varied, and in many cases still rather unclear. However, a possibly reasonable subgoal is that sensory information may be encoded efficiently in a population of neurons. In this context, Mutual Information is a l ...

IDIAP2004

From error probability to information theoretic signal and image processing

Torsten Butz

The signal processing community is increasingly interested in using information theoretic concepts to build signal processing algorithms for a variety of applications. A general theory on how to apply the mathematical concepts of information theory to the ...

EPFL2003

Variance-Covariance perturbation analysis of a Linear Model

Pierre-Yves Gilliéron, Bertrand Merminod

In this paper the perturbation influence properties of the stochastic model (variance-covariance) in linear models is discussed in detail. Some very useful formulae are established about the variance-covariance perturbation influence on the model parameter ...

2003

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000

Mixture Models for Unsupervised and Supervised Learning

École Polytechnique Fédérale de Lausanne, Computer Science Department2000