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2020 – today
- 2024
[c25]Jean-Baptiste Malagnoux, Matthieu Kowalski:
From Convolutional Sparse Coding To *-NMF Factorization of Time-Frequency Coefficients. ICASSP 2024: 5530-5534- 2023
- [j13]Diego Delle Donne, Matthieu Kowalski, Leo Liberti:
A Novel Integer Linear Programming Approach for Global L0 Minimization. J. Mach. Learn. Res. 24: 382:1-382:28 (2023) - [c24]Pierre Barbault, Matthieu Kowalski, Charles Soussen:
Marginal MAP Estimation of a Bernoulli-Gaussian Signal: Continuous Relaxation Approach. EUSIPCO 2023: 1833-1837 - [c23]Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. ICML 2023: 2777-2805 - [i5]Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty:
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals. CoRR abs/2303.05798 (2023) - 2022
- [c22]Pierre Barbault, Matthieu Kowalski, Charles Soussen:
Parameter Estimation in Sparse Inverse Problems Using Bernoulli-Gaussian Prior. ICASSP 2022: 5413-5417 - [c21]Benoît Malézieux, Thomas Moreau, Matthieu Kowalski:
Understanding approximate and unrolled dictionary learning for pattern recovery. ICLR 2022 - 2021
- [i4]Benoît Malézieux, Thomas Moreau, Matthieu Kowalski:
Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems. CoRR abs/2106.06338 (2021) - 2020
- [i3]Diego Delle Donne, Matthieu Kowalski, Leo Liberti:
MIP and Set Covering approaches for Sparse Approximation. CoRR abs/2009.06312 (2020)
2010 – 2019
- 2019
- [j12]Fangchen Feng
, Matthieu Kowalski:
Underdetermined Reverberant Blind Source Separation: Sparse Approaches for Multiplicative and Convolutive Narrowband Approximation. IEEE ACM Trans. Audio Speech Lang. Process. 27(2): 442-456 (2019) - 2018
- [b1]Matthieu Kowalski:
Sparse Wars. University of Paris-Sud, Orsay, France, 2018 - [j11]Alina-Georgiana Meresescu, Matthieu Kowalski, Frédéric Schmidt, François Landais:
Water Residence Time estimation by 1D deconvolution in the form of a l2-regularized inverse problem with smoothness, positivity and causality constraints. Comput. Geosci. 115: 105-121 (2018) - [j10]Fangchen Feng, Matthieu Kowalski:
Revisiting sparse ICA from a synthesis point of view: Blind Source Separation for over and underdetermined mixtures. Signal Process. 152: 165-177 (2018) - [j9]Clement Laroche, Matthieu Kowalski, Hélène Papadopoulos, Gaël Richard:
Hybrid Projective Nonnegative Matrix Factorization With Drum Dictionaries for Harmonic/Percussive Source Separation. IEEE ACM Trans. Audio Speech Lang. Process. 26(9): 1499-1511 (2018) - [j8]Cédric Févotte, Matthieu Kowalski:
Estimation With Low-Rank Time-Frequency Synthesis Models. IEEE Trans. Signal Process. 66(15): 4121-4132 (2018) - [i2]Cédric Févotte, Matthieu Kowalski:
Estimation with Low-Rank Time-Frequency Synthesis Models. CoRR abs/1804.09497 (2018) - 2017
- [c20]Clement Laroche, Hélène Papadopoulos, Matthieu Kowalski, Gaël Richard:
Drum extraction in single channel audio signals using multi-layer Non negative Matrix Factor Deconvolution. ICASSP 2017: 46-50 - [c19]Fangchen Feng, Matthieu Kowalski:
Sparsity and low-rank amplitude based blind Source Separation. ICASSP 2017: 571-575 - 2016
- [c18]Clement Laroche, Hélène Papadopoulos, Matthieu Kowalski, Gaël Richard:
Genre Specific Dictionaries for Harmonic/Percussive Source Separation. ISMIR 2016: 407-413 - [c17]Gaël Varoquaux, Matthieu Kowalski, Bertrand Thirion:
Social-sparsity brain decoders: faster spatial sparsity. PRNI 2016: 1-4 - [i1]Gaël Varoquaux, Matthieu Kowalski, Bertrand Thirion:
Social-sparsity brain decoders: faster spatial sparsity. CoRR abs/1606.06439 (2016) - 2015
- [c16]Cyril Gaudefroy, Hélène Papadopoulos, Matthieu Kowalski:
A multi-dimensional meter-adaptive method for automatic segmentation of music. CBMI 2015: 1-6 - [c15]Cédric Févotte, Matthieu Kowalski:
Hybrid sparse and low-rank time-frequency signal decomposition. EUSIPCO 2015: 464-468 - [c14]Fangchen Feng, Matthieu Kowalski:
An unified approach for blind source separation using sparsity and decorrelation. EUSIPCO 2015: 1736-1740 - [c13]Matthieu Kowalski, Alexandre Gramfort:
Inverse problems with time-frequency dictionaries and non-white Gaussian noise. EUSIPCO 2015: 1741-1745 - [c12]Clement Laroche, Matthieu Kowalski, Hélène Papadopoulos, Gaël Richard:
A structured nonnegative matrix factorization for source separation. EUSIPCO 2015: 2033-2037 - 2014
- [c11]Kai Siedenburg, Matthieu Kowalski, Monika Dörfler:
Audio declipping with social sparsity. ICASSP 2014: 1577-1578 - [c10]Fangchen Feng, Matthieu Kowalski:
Hybrid model and structured sparsity for under-determined convolutive audio source separation. ICASSP 2014: 6682-6686 - [c9]Matthieu Kowalski:
Thresholding RULES and iterative shrinkage/thresholding algorithm: A convergence study. ICIP 2014: 4151-4155 - [c8]Cédric Févotte, Matthieu Kowalski:
Low-Rank Time-Frequency Synthesis. NIPS 2014: 3563-3571 - 2013
- [j7]Alexandre Gramfort, Daniel Strohmeier, Jens Haueisen, Matti S. Hämäläinen, Matthieu Kowalski:
Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations. NeuroImage 70: 410-422 (2013) - [j6]Péter Balázs, Monika Dörfler, Matthieu Kowalski, Bruno Torrésani:
Adapted and Adaptive Linear Time-Frequency Representations: A Synthesis Point of View. IEEE Signal Process. Mag. 30(6): 20-31 (2013) - [j5]Matthieu Kowalski, Kai Siedenburg, Monika Dörfler:
Social Sparsity! Neighborhood Systems Enrich Structured Shrinkage Operators. IEEE Trans. Signal Process. 61(10): 2498-2511 (2013) - 2011
- [c7]Matthieu Kowalski, Thomas Rodet:
An unsupervised algorithm for hybrid/morphological signal decomposition. ICASSP 2011: 4112-4115 - [c6]Alexandre Gramfort, Daniel Strohmeier, Jens Haueisen, Matti S. Hämäläinen, Matthieu Kowalski:
Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries. IPMI 2011: 600-611 - [c5]Hélène Papadopoulos, Matthieu Kowalski:
Sparse Signal Decomposition on Hybrid Dictionaries Using Musical Priors. ISMIR 2011: 687-692 - 2010
- [j4]Matthieu Kowalski, Emmanuel Vincent, Rémi Gribonval:
Beyond the Narrowband Approximation: Wideband Convex Methods for Under-Determined Reverberant Audio Source Separation. IEEE Trans. Speech Audio Process. 18(7): 1818-1829 (2010) - [j3]Matthieu Kowalski, Alexandre Gramfort:
A priori par normes mixtes pour les problèmes inverses. Application à la localisation de sources en M/EEG. Traitement du Signal 27(1): 53-78 (2010)
2000 – 2009
- 2009
- [j2]Matthieu Kowalski, Bruno Torrésani:
Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients. Signal Image Video Process. 3(3): 251-264 (2009) - [c4]Matthieu Kowalski, Marie Szafranski, Liva Ralaivola:
Multiple indefinite kernel learning with mixed norm regularization. ICML 2009: 545-552 - [c3]Alexandre Gramfort, Matthieu Kowalski:
Improving M/EEG Source Localization with an Inter-Condition Sparse Prior. ISBI 2009: 141-144 - 2008
- [j1]Matthieu Kowalski, Bruno Torrésani:
Random Models for Sparse Signals Expansion on Unions of Bases With Application to Audio Signals. IEEE Trans. Signal Process. 56(8-1): 3468-3481 (2008) - [c2]Matthieu Kowalski, Emmanuel Vincent, Rémi Gribonval:
Under-determined source separation via mixed-norm regularized minimization. EUSIPCO 2008: 1-5 - 2006
- [c1]Matthieu Kowalski, Bruno Torrésani:
a Family of Random Waveform Models for Audio Coding. ICASSP (3) 2006: 472-475
Coauthor Index
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