D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 55 Citations 13,528 190 World Ranking 2167 National Ranking 35

Research.com Recognitions

Awards & Achievements

2014 - IEEE Fellow For contributions to the theory and applications of sparse signal processing

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Algorithm

Rémi Gribonval mainly investigates Sparse approximation, Source separation, Artificial intelligence, Algorithm and Pattern recognition. His studies deal with areas such as Discrete mathematics, Combinatorics, Inpainting, Greedy algorithm and Sparse matrix as well as Sparse approximation. His Source separation study combines topics from a wide range of disciplines, such as Audio signal processing, Estimation theory and Blind signal separation.

His Wavelet study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Envelope detector, bridging the gap between disciplines. His Algorithm research is mostly focused on the topic Matching pursuit. His work in the fields of Continuous wavelet transform overlaps with other areas such as Gaussian process.

His most cited work include:

  • Performance measurement in blind audio source separation (1896 citations)
  • Wavelets on graphs via spectral graph theory (1139 citations)
  • Sparse representations in unions of bases (877 citations)

What are the main themes of his work throughout his whole career to date?

Rémi Gribonval spends much of his time researching Artificial intelligence, Algorithm, Sparse approximation, Pattern recognition and Source separation. His work in Algorithm covers topics such as Mathematical optimization which are related to areas like Inverse problem and Convex optimization. Rémi Gribonval combines subjects such as Matching pursuit, Sparse matrix, Theoretical computer science and Signal processing with his study of Sparse approximation.

His work on Mixture model as part of general Pattern recognition study is frequently connected to Gaussian process, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Source separation research is multidisciplinary, incorporating elements of Audio signal processing, Covariance function and Blind signal separation. His Blind signal separation research incorporates elements of Estimation theory and Independent component analysis.

He most often published in these fields:

  • Artificial intelligence (31.56%)
  • Algorithm (30.33%)
  • Sparse approximation (25.00%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (31.56%)
  • Algorithm (30.33%)
  • Machine learning (7.38%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Machine learning, Estimator and Discrete mathematics. His studies in Artificial intelligence integrate themes in fields like User interface and Pattern recognition. Rémi Gribonval focuses mostly in the field of Pattern recognition, narrowing it down to matters related to White noise and, in some cases, Speech recognition.

His Algorithm research includes elements of k-means clustering, Sparse matrix and Fourier transform. His Discrete mathematics study incorporates themes from Harmonic wavelet transform, Fractional Fourier transform and Monotone polygon. A large part of his Sparse approximation studies is devoted to K-SVD.

Between 2016 and 2021, his most popular works were:

  • Approximate Fast Graph Fourier Transforms via Multilayer Sparse Approximations (35 citations)
  • Training with Quantization Noise for Extreme Model Compression (31 citations)
  • Approximation spaces of deep neural networks (29 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Algorithm

Rémi Gribonval spends much of his time researching Algorithm, Estimator, Quantization, Regularization and Contextual image classification. He interconnects Sparse matrix, k-means clustering, Cluster analysis and Operator in the investigation of issues within Algorithm. His Estimator study integrates concerns from other disciplines, such as Scale, Probability distribution, Outcome and Applied mathematics.

His work deals with themes such as Flow, Model compression and Noise, which intersect with Quantization. The study incorporates disciplines such as Domain, Optimization problem and Second-order cone programming in addition to Regularization. Contextual image classification is closely attributed to Compression in his study.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Performance measurement in blind audio source separation

E. Vincent;R. Gribonval;C. Fevotte.
IEEE Transactions on Audio, Speech, and Language Processing (2006)

2627 Citations

Wavelets on graphs via spectral graph theory

David K. Hammond;Pierre Vandergheynst;Rémi Gribonval.
Applied and Computational Harmonic Analysis (2011)

1420 Citations

Sparse representations in unions of bases

R. Gribonval;M. Nielsen.
IEEE Transactions on Information Theory (2003)

1023 Citations

The Cosparse Analysis Model and Algorithms

Sangnam Nam;Mike E. Davies;Michael Elad;Rémi Gribonval.
Applied and Computational Harmonic Analysis (2013)

429 Citations

Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model

Ngoc Q K Duong;Emmanuel Vincent;Rémi Gribonval.
IEEE Transactions on Audio, Speech, and Language Processing (2010)

414 Citations

Harmonic decomposition of audio signals with matching pursuit

R. Gribonval;E. Bacry.
IEEE Transactions on Signal Processing (2003)

311 Citations

Fast matching pursuit with a multiscale dictionary of Gaussian chirps

R. Gribonval.
IEEE Transactions on Signal Processing (2001)

286 Citations

Sparse Representations in Audio and Music: From Coding to Source Separation

Mark D Plumbley;Thomas Blumensath;Laurent Daudet;Remi Gribonval.
Proceedings of the IEEE (2010)

268 Citations

BSS_EVAL Toolbox User Guide -- Revision 2.0

Cédric Févotte;Rémi Gribonval;Emmanuel Vincent.
(2005)

259 Citations

On the exponential convergence of matching pursuits in quasi-incoherent dictionaries

R. Gribonval;P. Vandergheynst.
IEEE Transactions on Information Theory (2006)

239 Citations

Best Scientists Citing Rémi Gribonval

Mark D. Plumbley

Mark D. Plumbley

University of Surrey

Publications: 89

Emmanuel Vincent

Emmanuel Vincent

University of Lorraine

Publications: 83

Pierre Vandergheynst

Pierre Vandergheynst

École Polytechnique Fédérale de Lausanne

Publications: 83

Michael Elad

Michael Elad

Technion – Israel Institute of Technology

Publications: 76

Wenwu Wang

Wenwu Wang

University of Surrey

Publications: 70

Tomohiro Nakatani

Tomohiro Nakatani

NTT (Japan)

Publications: 66

Gael Richard

Gael Richard

Télécom ParisTech

Publications: 62

Hiroshi Saruwatari

Hiroshi Saruwatari

University of Tokyo

Publications: 61

Tuomas Virtanen

Tuomas Virtanen

Tampere University

Publications: 57

Pascal Frossard

Pascal Frossard

École Polytechnique Fédérale de Lausanne

Publications: 55

Roland Badeau

Roland Badeau

Télécom ParisTech

Publications: 54

mike davies

mike davies

University of Edinburgh

Publications: 50

Antonio Ortega

Antonio Ortega

University of Southern California

Publications: 49

Paris Smaragdis

Paris Smaragdis

University of Illinois at Urbana-Champaign

Publications: 49

Shoko Araki

Shoko Araki

NTT (Japan)

Publications: 43

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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