Pierre Chainais
Professeur des universités
Laboratoire / équipe
Publications
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"citationFull_s":"Lucas Einig, Pierre Palud, Antoine Roueff, Jérôme Pety, Emeric E. Bron, et al.. Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds. <i>Astronomy and Astrophysics - A&A</i>, 2024, 691 (A109), <a target=\"_blank\" href=\"https://dx.doi.org/10.1051/0004-6361/202451588\">⟨10.1051/0004-6361/202451588⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04654762v1\">⟨hal-04654762⟩</a>",
"title_s":["Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds","Quantifier l'informativité des raies d'émission afin de déduire les conditions physiques au sein des nuages moléculaires géants"],
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"citationRef_s":"<i>Machine Learning</i>, 2024, <a target=\"_blank\" href=\"https://dx.doi.org/10.1007/s10994-024-06623-x\">⟨10.1007/s10994-024-06623-x⟩</a>",
"citationFull_s":"Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais. Normalizing flow sampling with Langevin dynamics in the latent space. <i>Machine Learning</i>, 2024, <a target=\"_blank\" href=\"https://dx.doi.org/10.1007/s10994-024-06623-x\">⟨10.1007/s10994-024-06623-x⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04710673v1\">⟨hal-04710673⟩</a>",
"title_s":["Normalizing flow sampling with Langevin dynamics in the latent space"],
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"citationFull_s":"Antoine Roueff, Jérôme Pety, Maryvonne Gerin, Léontine Ségal, Javier Goicoechea, et al.. Bias versus variance when fitting multi-species molecular lines with a non-LTE radiative transfer model. <i>Astronomy and Astrophysics - A&A</i>, 2024, 686 (June), pp.A255. <a target=\"_blank\" href=\"https://dx.doi.org/10.1051/0004-6361/202449148\">⟨10.1051/0004-6361/202449148⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04523850v1\">⟨hal-04523850⟩</a>",
"title_s":["Bias versus variance when fitting multi-species molecular lines with a non-LTE radiative transfer model"],
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"citationRef_s":"<i>IEEE Transactions on Image Processing</i>, 2024, 33, pp.3496-3507. <a target=\"_blank\" href=\"https://dx.doi.org/10.1109/TIP.2024.3404338\">⟨10.1109/TIP.2024.3404338⟩</a>",
"citationFull_s":"Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais. Plug-and-Play Split Gibbs Sampler: Embedding Deep Generative Priors in Bayesian Inference. <i>IEEE Transactions on Image Processing</i>, 2024, 33, pp.3496-3507. <a target=\"_blank\" href=\"https://dx.doi.org/10.1109/TIP.2024.3404338\">⟨10.1109/TIP.2024.3404338⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04602765v1\">⟨hal-04602765⟩</a>",
"title_s":["Plug-and-Play Split Gibbs Sampler: Embedding Deep Generative Priors in Bayesian Inference"],
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"citationFull_s":"Pierre Palud, Pierre-Antoine Thouvenin, Pierre Chainais, Emeric E. Bron, Franck Le Petit. Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS). 2024. <a target=\"_blank\" href=\"https://hal.science/hal-04461075v1\">⟨hal-04461075⟩</a>",
"title_s":["Beetroots (BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS)"],
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"citationFull_s":"Clémence Prévost, Pierre Chainais. HIGH-DIMENSIONAL, LOW-RANK TENSOR APPROXIMATION: CRAMÉR-RAO LOWER BOUNDS AND APPLICATION TO MIMO CHANNELS. 2023. <a target=\"_blank\" href=\"https://hal.science/hal-04302405v1\">⟨hal-04302405⟩</a>",
"title_s":["HIGH-DIMENSIONAL, LOW-RANK TENSOR APPROXIMATION: CRAMÉR-RAO LOWER BOUNDS AND APPLICATION TO MIMO CHANNELS"],
"authFullName_s":["Clémence Prévost","Pierre Chainais"],
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"citationRef_s":"<i>Journal of Computational and Graphical Statistics</i>, 2023, pp.1-35. <a target=\"_blank\" href=\"https://dx.doi.org/10.1080/10618600.2023.2282501\">⟨10.1080/10618600.2023.2282501⟩</a>",
"citationFull_s":"Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais. A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems. <i>Journal of Computational and Graphical Statistics</i>, 2023, pp.1-35. <a target=\"_blank\" href=\"https://dx.doi.org/10.1080/10618600.2023.2282501\">⟨10.1080/10618600.2023.2282501⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04267086v1\">⟨hal-04267086⟩</a>",
"title_s":["A Distributed Block-Split Gibbs Sampler with Hypergraph Structure for High-Dimensional Inverse Problems"],
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"citationFull_s":"M G Santa-Maria, J R Goicoechea, J Pety, M Gerin, J H Orkisz, et al.. HCN emission from translucent gas and UV-illuminated cloud edges revealed by wide-field IRAM 30 m maps of the Orion B GMC. <i>Astronomy and Astrophysics - A&A</i>, 2023, 679, pp.A4. <a target=\"_blank\" href=\"https://dx.doi.org/10.1051/0004-6361/202346598\">⟨10.1051/0004-6361/202346598⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04417167v1\">⟨hal-04417167⟩</a>",
"title_s":["HCN emission from translucent gas and UV-illuminated cloud edges revealed by wide-field IRAM 30 m maps of the Orion B GMC"],
"authFullName_s":["M G Santa-Maria","J R Goicoechea","J Pety","M Gerin","J H Orkisz","F Le Petit","L Einig","P Palud","V de Souza Magalhaes","I Bešlić","L Segal","S Bardeau","E Bron","P Chainais","J Chanussot","P Gratier","V V Guzmán","A Hughes","D Languignon","F Levrier","D C Lis","H S Liszt","J Le Bourlot","Y Oya","K Öberg","N Peretto","E Roueff","A Roueff","A Sievers","P-A Thouvenin","S Yamamoto"],
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"citationFull_s":"Lucas Einig, Jérôme Pety, Antoine Roueff, Paul Vandame, Jocelyn Chanussot, et al.. Deep learning denoising by dimension reduction: Application to the ORION-B line cubes. <i>Astronomy and Astrophysics - A&A</i>, 2023, 677 (A158), <a target=\"_blank\" href=\"https://dx.doi.org/10.1051/0004-6361/202346064\">⟨10.1051/0004-6361/202346064⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04167877v2\">⟨hal-04167877v2⟩</a>",
"title_s":["Deep learning denoising by dimension reduction: Application to the ORION-B line cubes"],
"authFullName_s":["Lucas Einig","Jérôme Pety","Antoine Roueff","Paul Vandame","Jocelyn Chanussot","Maryvonne Gerin","Jan H. Orkisz","Pierre Palud","Miriam G. Santa-Maria","Victor de Souza Magalhaes","Ivana Bešlić","Sébastien Bardeau","Emeric E. Bron","Pierre Chainais","Javier R Goicoechea","Pierre Gratier","Viviana Guzman Veloso","Annie Hughes","Jouni Kainulainen","David Languignon","Rosine Lallement","François Levrier","Dariuscz C. Lis","Harvey Liszt","Jacques Le Bourlot","Franck Le Petit","Karin Danielsson Öberg","Nicolas Peretto","Evelyne Roueff","Albrecht Sievers","Pierre-Antoine Thouvenin","Pascal Tremblin"],
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"citationRef_s":"<i>IEEE Signal Processing Magazine</i>, 2023, 40 (6), pp.26-40. <a target=\"_blank\" href=\"https://dx.doi.org/10.1109/MSP.2023.3278071\">⟨10.1109/MSP.2023.3278071⟩</a>",
"citationFull_s":"Sebastian Miron, Julien Flamant, Nicolas Le Bihan, Pierre Chainais, David Brie. Quaternions in signal and image processing: A comprehensive and objective overview. <i>IEEE Signal Processing Magazine</i>, 2023, 40 (6), pp.26-40. <a target=\"_blank\" href=\"https://dx.doi.org/10.1109/MSP.2023.3278071\">⟨10.1109/MSP.2023.3278071⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04199725v1\">⟨hal-04199725⟩</a>",
"title_s":["Quaternions in signal and image processing: A comprehensive and objective overview"],
"authFullName_s":["Sebastian Miron","Julien Flamant","Nicolas Le Bihan","Pierre Chainais","David Brie"],
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"title_s":["Méthode MCMC plug-and-play avec a priori génératif profond"],
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"citationRef_s":"<i>29° Colloque sur le traitement du signal et des images</i>, GRETSI - Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. pp.697 - 700",
"citationFull_s":"Pierre Palud, Pierre-Antoine Thouvenin, Pierre Chainais, Emeric E. Bron, Franck Le Petit. Problèmes inverses et test bayésien d'adéquation du modèle. <i>29° Colloque sur le traitement du signal et des images</i>, GRETSI - Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. pp.697 - 700. <a target=\"_blank\" href=\"https://hal.science/hal-04479586v1\">⟨hal-04479586⟩</a>",
"title_s":["Problèmes inverses et test bayésien d'adéquation du modèle"],
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"citationFull_s":"Lucas Einig, Pierre Palud, Jocelyn Chanussot, Jérôme Pety, Emeric E. Bron, et al.. Réduction d’un modèle astrophysique par réseaux de neurones. <i>GRETSI 2023 - XXIXème Colloque Francophone de Traitement du Signal et des Images</i>, GRETSI - Groupe de Recherche en Traitement du Signal et des Images, Aug 2023, Grenoble, France. <a target=\"_blank\" href=\"https://hal.science/hal-04253770v1\">⟨hal-04253770⟩</a>",
"title_s":["Réduction d’un modèle astrophysique par réseaux de neurones","Neural network-based emulation of astrophysical models"],
"authFullName_s":["Lucas Einig","Pierre Palud","Jocelyn Chanussot","Jérôme Pety","Emeric E. Bron","Pierre Chainais","Franck Le Petit","Pierre-Antoine Thouvenin","Maryvonne Gerin","Antoine Roueff","Ivana Bešlić","Miriam G. Santa-Maria","Jan H. Orkisz","Antoine Zakardjian","Sébastien Bardeau","Javier R. Goicoechea","Pierre Gratier","Viviana V. Guzman","Annie Hughes","François Levrier","Karin D. Öberg","Nicolas Peretto","Evelyne Roueff","Albrecht Sievers"],
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"title_s":["dsgs: a Distributed block-Split-Gibbs Sampler (DSGS) with hypergraph structure for high-dimensional inverse problems"],
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"citationFull_s":"Pierre Palud, Pierre-Antoine Thouvenin, Pierre Chainais, Emeric Bron, Franck Le Petit. Efficient Sampling of Non Log-Concave Posterior Distributions With Mixture of Noises. <i>IEEE Transactions on Signal Processing</i>, 2023, 71, pp.2491-2501. <a target=\"_blank\" href=\"https://dx.doi.org/10.1109/TSP.2023.3289728\">⟨10.1109/TSP.2023.3289728⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-04264362v1\">⟨hal-04264362⟩</a>",
"title_s":["Efficient Sampling of Non Log-Concave Posterior Distributions With Mixture of Noises"],
"authFullName_s":["Pierre Palud","Pierre-Antoine Thouvenin","Pierre Chainais","Emeric Bron","Franck Le Petit"],
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"citationRef_s":"<i>30th European Symposium on Artificial Neural Networks (ESANN 2022)</i>, Oct 2022, Bruges, Belgium. <a target=\"_blank\" href=\"https://dx.doi.org/10.48550/arXiv.2207.05468\">⟨10.48550/arXiv.2207.05468⟩</a>",
"citationFull_s":"Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais. Sliced-Wasserstein normalizing flows: beyond maximum likelihood training. <i>30th European Symposium on Artificial Neural Networks (ESANN 2022)</i>, Oct 2022, Bruges, Belgium. <a target=\"_blank\" href=\"https://dx.doi.org/10.48550/arXiv.2207.05468\">⟨10.48550/arXiv.2207.05468⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-03720995v1\">⟨hal-03720995⟩</a>",
"title_s":["Sliced-Wasserstein normalizing flows: beyond maximum likelihood training"],
"authFullName_s":["Florentin Coeurdoux","Nicolas Dobigeon","Pierre Chainais"],
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"citationRef_s":"<i>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022)</i>, Sep 2022, Grenoble, France. <a target=\"_blank\" href=\"https://dx.doi.org/10.48550/arXiv.2207.01246\">⟨10.48550/arXiv.2207.01246⟩</a>",
"citationFull_s":"Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais. Learning optimal transport between two empirical distributions with normalizing flows. <i>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2022)</i>, Sep 2022, Grenoble, France. <a target=\"_blank\" href=\"https://dx.doi.org/10.48550/arXiv.2207.01246\">⟨10.48550/arXiv.2207.01246⟩</a>. <a target=\"_blank\" href=\"https://hal.science/hal-03713840v1\">⟨hal-03713840⟩</a>",
"title_s":["Learning optimal transport between two empirical distributions with normalizing flows"],
"authFullName_s":["Florentin Coeurdoux","Nicolas Dobigeon","Pierre Chainais"],
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"citationFull_s":"Pierre Palud, Pierre Chainais, Franck Le Petit, Emeric Bron, Pierre-Antoine Thouvenin, et al.. Mélange de bruits et échantillonnage de posterior non log-concave. <i>XXVIIIème Colloque Francophone de Traitement du Signal et des Images (GRETSI 2022)</i>, Association GRETSI : Groupe de Recherche et d'Etudes de Traitement du Signal et des Images, Sep 2022, Nancy, France. <a target=\"_blank\" href=\"https://hal.science/hal-03952718v1\">⟨hal-03952718⟩</a>",
"title_s":["Mélange de bruits et échantillonnage de posterior non log-concave"],
"authFullName_s":["Pierre Palud","Pierre Chainais","Franck Le Petit","Emeric Bron","Pierre-Antoine Thouvenin","Maxime Vono","Lucas Einig","Miriam Garcia Santa-Maria","Mathilde Gaudel","Jan Orkisz","Victor de Souza Magalhaes","Sébastien Bardeau","Maryvonne Gerin","Javier R Goicoechea","Pierre Gratier","Viviana V. Guzman","Jouni Kainulainen","François Levrier","Nicolas Peretto","Jérôme Pety","Antoine Roueff","Albrecht Sievers"],
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"citationFull_s":"Juan M Miramont, Rémi Bardenet, Pierre Chainais, François Auger. A public benchmark for denoising and detection methods. <i>XXVIIIème Colloque Francophone du GRETSI</i>, GRETSI, Sep 2022, Nancy, France. pp.1-4. <a target=\"_blank\" href=\"https://hal.science/hal-04102094v1\">⟨hal-04102094⟩</a>",
"title_s":["A public benchmark for denoising and detection methods"],
"authFullName_s":["Juan M Miramont","Rémi Bardenet","Pierre Chainais","François Auger"],
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"citationFull_s":"Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais. Approximation du transport optimal entre distributions empiriques par flux de normalisation. <i>XXVIIIème Colloque Francophone de Traitement du Signal et des Images (GRETSI 2022)</i>, Sep 2022, Nancy, France. <a target=\"_blank\" href=\"https://hal.science/hal-03704666v1\">⟨hal-03704666⟩</a>",
"title_s":["Approximation du transport optimal entre distributions empiriques par flux de normalisation"],
"authFullName_s":["Florentin Coeurdoux","Nicolas Dobigeon","Pierre Chainais"],
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"citationRef_s":"<i>XXIXième Colloque GRETSI</i>, Sep 2022, Nancy, France",
"citationFull_s":"Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais. Un algorithme MCMC distribué pour la résolution de problèmes inverses de grande dimension. <i>XXIXième Colloque GRETSI</i>, Sep 2022, Nancy, France. <a target=\"_blank\" href=\"https://hal.science/hal-03718793v1\">⟨hal-03718793⟩</a>",
"title_s":["Un algorithme MCMC distribué pour la résolution de problèmes inverses de grande dimension"],
"authFullName_s":["Pierre-Antoine Thouvenin","Audrey Repetti","Pierre Chainais"],
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