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Books and Theses
- 2009
[b1]Pierre Chainais:
Processus aléatoires invariants d'échelle et analyse multirésolution pour la modélisation d'observations de systèmes physiques. (Scale invariant stochastic processes and multiresolution analysis for the modeling of physical systems). Blaise Pascal University, Clermont-Ferrand, France, 2009
Journal Articles
- 2024
- [j19]Markus Grimm
, Sébastien Paul, Pierre Chainais:
Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems. Comput. Chem. Eng. 189: 108779 (2024) - [j18]Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Plug-and-Play Split Gibbs Sampler: Embedding Deep Generative Priors in Bayesian Inference. IEEE Trans. Image Process. 33: 3496-3507 (2024) - 2023
- [j17]Sebastian Miron, Julien Flamant, Nicolas Le Bihan, Pierre Chainais, David Brie:
Quaternions in Signal and Image Processing: A comprehensive and objective overview. IEEE Signal Process. Mag. 40(6): 26-40 (2023) - [j16]Pierre Palud, Pierre-Antoine Thouvenin, Pierre Chainais, Emeric Bron, Franck Le Petit:
Efficient Sampling of Non Log-Concave Posterior Distributions With Mixture of Noises. IEEE Trans. Signal Process. 71: 2491-2501 (2023) - 2022
- [j15]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm. SIAM Rev. 64(1): 3-56 (2022) - 2021
- [j14]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Asymptotically Exact Data Augmentation: Models, Properties, and Algorithms. J. Comput. Graph. Stat. 30(2): 335-348 (2021) - 2020
- [j13]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
A determinantal point process for column subset selection. J. Mach. Learn. Res. 21: 197:1-197:62 (2020) - 2019
- [j12]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Split-and-Augmented Gibbs Sampler - Application to Large-Scale Inference Problems. IEEE Trans. Signal Process. 67(6): 1648-1661 (2019) - 2018
- [j11]Julien Flamant, Pierre Chainais, Nicolas Le Bihan:
A Complete Framework for Linear Filtering of Bivariate Signals. IEEE Trans. Signal Process. 66(17): 4541-4552 (2018) - [j10]Hong Phuong Dang, Pierre Chainais:
Towards Dictionaries of Optimal Size: A Bayesian Non Parametric Approach. J. Signal Process. Syst. 90(2): 221-232 (2018) - 2017
- [j9]Hong Phuong Dang, Pierre Chainais:
Indian Buffet Process dictionary learning: Algorithms and applications to image processing. Int. J. Approx. Reason. 83: 1-20 (2017) - [j8]Clement Elvira, Pierre Chainais, Nicolas Dobigeon:
Bayesian Antisparse Coding. IEEE Trans. Signal Process. 65(7): 1660-1672 (2017) - [j7]Julien Flamant, Nicolas Le Bihan, Pierre Chainais:
Spectral Analysis of Stationary Random Bivariate Signals. IEEE Trans. Signal Process. 65(23): 6135-6145 (2017) - 2016
- [j6]Pierre Chainais, Aymeric Leray:
Statistical Performance Analysis of a Fast Super-Resolution Technique Using Noisy Translations. IEEE Trans. Image Process. 25(4): 1699-1712 (2016) - 2011
- [j5]Pierre Chainais, Emilie Koenig, Véronique Delouille, Jean-François Hochedez:
Virtual Super Resolution of Scale Invariant Textured Images Using Multifractal Stochastic Processes. J. Math. Imaging Vis. 39(1): 28-44 (2011) - 2009
- [j4]Patrice Abry, Pierre Chainais, Laure Coutin, Vladas Pipiras:
Multifractal random walks as fractional Wiener integrals. IEEE Trans. Inf. Theory 55(8): 3825-3846 (2009) - 2007
- [j3]Pierre Chainais:
Infinitely Divisible Cascades to Model the Statistics of Natural Images. IEEE Trans. Pattern Anal. Mach. Intell. 29(12): 2105-2119 (2007) - 2005
- [j2]Pierre Chainais, Rudolf H. Riedi, Patrice Abry:
On non-scale-invariant infinitely divisible cascades. IEEE Trans. Inf. Theory 51(3): 1063-1083 (2005) - 2004
- [j1]Bruno Lashermes, Patrice Abry, Pierre Chainais:
New Insights into the Estimation of Scaling Exponents. Int. J. Wavelets Multiresolution Inf. Process. 2(4): 497-523 (2004)
Conference and Workshop Papers
- 2024
- [c33]Yusuf Yigit Pilavci, Jérémie Boulanger, Pierre-Antoine Thouvenin, Pierre Chainais:
Denoising Bivariate Signals via Smoothing and Polarization Priors. EUSIPCO 2024: 2602-2606 - 2022
- [c32]Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training. ESANN 2022 - [c31]Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais:
A versatile distributed MCMC algorithm for large scale inverse problems. EUSIPCO 2022: 2016-2020 - [c30]Pierre Palud, Pierre Chainais, Franck Le Petit, Emeric Bron, Pierre-Antoine Thouvenin, Maxime Vono, L. Einig, M. Garcia Santa-Maria, Mathilde Gaudel, Jan H. Orkisz, Victor de Souza Magalhaes, Sébastien Bardeau, Maryvonne Gerin, Javier R. Goicoechea, Pierre Gratier, Viviana V. Guzmán, Jouni Kainulainen, François Levrier, Nicolas Peretto, Jérome Pety, Antoine Roueff, Albrecht Sievers:
Mixture of noises and sampling of non-log-concave posterior distributions. EUSIPCO 2022: 2031-2035 - [c29]Clémence Prévost, Pierre Chainais, Rémy Boyer:
Fast Fusion of Hyperspectral and Multispectral Images: A Tucker Approximation Approach. ICIP 2022: 2076-2080 - [c28]Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Learning Optimal Transport Between Two Empirical Distributions with Normalizing Flows. ECML/PKDD (5) 2022: 275-290 - 2020
- [c27]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel interpolation with continuous volume sampling. ICML 2020: 725-735 - 2019
- [c26]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Bayesian Image Restoration under Poisson Noise and Log-concave Prior. ICASSP 2019: 1712-1716 - [c25]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models. ICASSP 2019: 5037-5041 - [c24]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel quadrature with DPPs. NeurIPS 2019: 12907-12917 - [c23]Maxime Vono, Javier R. Goicoechea, Pierre Gratier, Viviana V. Guzmán, Annie Hughes, Jouni Kainulainen, David Languignon, Jacques Le Bourlot, François Levrier, Harvey S. Listz, Karin I. Oberg, Emeric Bron, Jan H. Orkisz, Nicolas Peretto, Jérome Pety, Antoine Roueff, Èvelyne Roueff, Albrecht Sievers, Victor de Souza Magalhaes, Pascal Tremblin, Pierre Chainais, Franck Le Petit, Sébastien Bardeau, Sébastien Bourguignon, Jocelyn Chanussot, Mathilde Gaudel, Maryvonne Gerin:
A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data. WHISPERS 2019: 1-5 - 2018
- [c22]Clement Elvira, Hong Phuong Dang, Pierre Chainais:
Small variance asymptotics and bayesian nonparametrics for dictionary learning. EUSIPCO 2018: 1607-1611 - [c21]Julien Flamant, Pierre Chainais, Éric Chassande-Mottin, Fangchen Feng, Nicolas Le Bihan:
Non-parametric characterization of gravitational-wave polarizations. EUSIPCO 2018: 2658-2662 - [c20]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Sparse Bayesian Binary logistic Regression using the Split-and-Augmented Gibbs sampler. MLSP 2018: 1-6 - [c19]Julien Flamant, Pierre Chainais, Nicolas Le Bihan:
Linear Filtering of Bivariate Signals Using Quaternions. SSP 2018: 154-158 - 2017
- [c18]Clement Elvira, Pierre Chainais, Nicolas Dobigeon:
Bayesian nonparametric subspace estimation. ICASSP 2017: 2247-2251 - [c17]Julien Flamant, Pierre Chainais, Nicolas Le Bihan:
Polarization spectrogram of bivariate signals. ICASSP 2017: 3989-3993 - 2016
- [c16]Hong Phuong Dang, Pierre Chainais:
Indian Buffet process dictionary learning for image inpainting. SSP 2016: 1-5 - [c15]Clement Elvira, Pierre Chainais, Nicolas Dobigeon:
Democratic prior for anti-sparse coding. SSP 2016: 1-4 - 2015
- [c14]Sylvain Rousseau, Pierre Chainais, Christelle Garnier:
Dictionary learning for a sparse appearance model in visual tracking. ICIP 2015: 4506-4510 - [c13]Hong Phuong Dang, Pierre Chainais:
A Bayesian non parametric approach to learn dictionaries with adapted numbers of atoms. MLSP 2015: 1-6 - 2014
- [c12]Pierre Chainais, Pierre Pfennig, Aymeric Leray:
Quantitative control of the error bounds of a fast super-resolution technique for microscopy and astronomy. ICASSP 2014: 2853-2857 - 2013
- [c11]Pierre Chainais, Cédric Richard:
Learning a common dictionary over a sensor network. CAMSAP 2013: 133-136 - 2012
- [c10]Pierre Chainais:
Towards dictionary learning from images with non Gaussian noise. MLSP 2012: 1-6 - 2011
- [c9]Pierre Chainais, Véronique Delouille, Jean-François Hochedez:
Scale invariant images in astronomy through the lens of multifractal modeling. ICIP 2011: 1309-1312 - 2009
- [c8]Emilie Koenig, Pierre Chainais:
Virtual resolution enhancement of scale invariant textured images using stochastic processes. ICIP 2009: 3137-3140 - 2008
- [c7]Emilie Koenig, Pierre Chainais:
Multifractal Analysis on the Sphere. ICISP 2008: 613-621 - 2006
- [c6]Alexandre Aussem, Pierre Chainais:
Modelling switching dynamics using prediction experts operating on distinct wavelet scales. ESANN 2006: 185-190 - [c5]Zahra Hamou Mamar, Pierre Chainais, Alexandre Aussem:
Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system. ESANN 2006: 659-664 - 2005
- [c4]Pierre Chainais:
Multi-dimensional infinitely divisible cascades to model the statistics of natural images. ICIP (3) 2005: 129-132 - 2004
- [c3]Bruno Lashermes, Patrice Abry, Pierre Chainais:
Scaling exponents estimation for multiscaling processes. ICASSP (2) 2004: 509-512 - 2000
- [c2]Pierre Chainais, Patrice Abry, Darryl Veitch:
Multifractal analysis and α-stable processes: a methodological contribution. ICASSP 2000: 241-244 - [c1]Darryl Veitch, Patrice Abry, Patrick Flandrin, Pierre Chainais:
Infinitely divisible cascade analysis of network traffic data. ICASSP 2000: 245-248
Informal and Other Publications
- 2024
- [i14]Juan Manuel Miramont, Rémi Bardenet, Pierre Chainais, François Auger:
Benchmarking multi-component signal processing methods in the time-frequency plane. CoRR abs/2402.08521 (2024) - [i13]Markus Grimm, Sébastien Paul, Pierre Chainais:
Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems. CoRR abs/2408.02551 (2024) - 2023
- [i12]Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference. CoRR abs/2304.11134 (2023) - [i11]Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Normalizing flow sampling with Langevin dynamics in the latent space. CoRR abs/2305.12149 (2023) - [i10]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Signal reconstruction using determinantal sampling. CoRR abs/2310.09437 (2023) - 2022
- [i9]Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows. CoRR abs/2207.01246 (2022) - [i8]Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais:
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training. CoRR abs/2207.05468 (2022) - [i7]Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais:
A distributed Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems. CoRR abs/2210.02341 (2022) - 2020
- [i6]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel interpolation with continuous volume sampling. CoRR abs/2002.09677 (2020) - 2019
- [i5]Maxime Vono, Nicolas Dobigeon, Pierre Chainais:
Asymptotically exact data augmentation: models, properties and algorithms. CoRR abs/1902.05754 (2019) - [i4]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
Kernel quadrature with DPPs. CoRR abs/1906.07832 (2019) - 2018
- [i3]Ayoub Belhadji, Rémi Bardenet, Pierre Chainais:
A determinantal point process for column subset selection. CoRR abs/1812.09771 (2018) - 2014
- [i2]Laurent Jacques, Christophe De Vleeschouwer, Yannick Boursier, Prasad Sudhakar, C. De Mol, Aleksandra Pizurica, Sandrine Anthoine, Pierre Vandergheynst, Pascal Frossard, Cagdas Bilen, Srdan Kitic, Nancy Bertin, Rémi Gribonval, Nicolas Boumal, Bamdev Mishra, Pierre-Antoine Absil, Rodolphe Sepulchre, Shaun Bundervoet, Colas Schretter, Ann Dooms, Peter Schelkens, Olivier Chabiron, François Malgouyres, Jean-Yves Tourneret, Nicolas Dobigeon, Pierre Chainais, Cédric Richard, Bruno Cornelis, Ingrid Daubechies, David B. Dunson, Marie Danková, Pavel Rajmic, Kévin Degraux, Valerio Cambareri, Bert Geelen, Gauthier Lafruit, Gianluca Setti, Jean-François Determe, Jérôme Louveaux, François Horlin, Angélique Drémeau, Patrick Héas, Cédric Herzet, Vincent Duval, Gabriel Peyré, Alhussein Fawzi, Mike E. Davies, Nicolas Gillis, Stephen A. Vavasis, Charles Soussen, Luc Le Magoarou, Jingwei Liang, Jalal Fadili, Antoine Liutkus, David Martina, Sylvain Gigan, Laurent Daudet, Mauro Maggioni, Stanislav Minsker, Nate Strawn, C. Mory, Fred Maurice Ngolè Mboula, Jean-Luc Starck, Ignace Loris, Samuel Vaiter, Mohammad Golbabaee, Dejan Vukobratovic:
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14). CoRR abs/1410.0719 (2014) - 2013
- [i1]Pierre Chainais, Cédric Richard:
Distributed dictionary learning over a sensor network. CoRR abs/1304.3568 (2013)
Coauthor Index
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