Pierre Chainais

1.1k total citations
51 papers, 525 citations indexed

About

Pierre Chainais is a scholar working on Computer Vision and Pattern Recognition, Economics and Econometrics and Finance. According to data from OpenAlex, Pierre Chainais has authored 51 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 15 papers in Economics and Econometrics and 10 papers in Finance. Recurrent topics in Pierre Chainais's work include Image and Signal Denoising Methods (17 papers), Complex Systems and Time Series Analysis (15 papers) and Financial Risk and Volatility Modeling (9 papers). Pierre Chainais is often cited by papers focused on Image and Signal Denoising Methods (17 papers), Complex Systems and Time Series Analysis (15 papers) and Financial Risk and Volatility Modeling (9 papers). Pierre Chainais collaborates with scholars based in France, United States and Belgium. Pierre Chainais's co-authors include Patrice Abry, Bruno Lashermes, Rudolf H. Riedi, Nicolas Dobigeon, Nicolas Le Bihan, J.‐F. Hochedez, Darryl Veitch, Jean-François Pinton, Patrick Flandrin and S. Gissot and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Information Theory and IEEE Transactions on Image Processing.

In The Last Decade

Pierre Chainais

45 papers receiving 499 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Pierre Chainais France 12 210 113 101 98 71 51 525
Pertti Mattila Finland 23 127 0.6× 124 1.1× 57 0.6× 286 2.9× 42 0.6× 72 3.2k
Hendrik Weber United Kingdom 14 99 0.5× 75 0.7× 179 1.8× 71 0.7× 37 0.5× 43 654
Jean‐Pierre Kahane France 17 76 0.4× 89 0.8× 200 2.0× 83 0.8× 74 1.0× 79 1.8k
Aline Bonami France 18 62 0.3× 145 1.3× 108 1.1× 23 0.2× 61 0.9× 63 1.2k
A. M. Iaglom 4 92 0.4× 32 0.3× 77 0.8× 55 0.6× 86 1.2× 7 623
Marcelo P. de Albuquerque Brazil 11 76 0.4× 332 2.9× 20 0.2× 60 0.6× 147 2.1× 33 834
Jacques Istas France 13 309 1.5× 54 0.5× 425 4.2× 36 0.4× 55 0.8× 34 754
Jean‐François Bercher France 16 125 0.6× 39 0.3× 36 0.4× 275 2.8× 173 2.4× 64 860
François Roueff France 16 264 1.3× 93 0.8× 252 2.5× 37 0.4× 130 1.8× 60 740
Grace Chan Australia 6 112 0.5× 25 0.2× 94 0.9× 58 0.6× 69 1.0× 8 404

Countries citing papers authored by Pierre Chainais

Since Specialization
Citations

This map shows the geographic impact of Pierre Chainais's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pierre Chainais with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pierre Chainais more than expected).

Fields of papers citing papers by Pierre Chainais

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pierre Chainais. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pierre Chainais. The network helps show where Pierre Chainais may publish in the future.

Co-authorship network of co-authors of Pierre Chainais

This figure shows the co-authorship network connecting the top 25 collaborators of Pierre Chainais. A scholar is included among the top collaborators of Pierre Chainais based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pierre Chainais. Pierre Chainais is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Chainais, Pierre, et al.. (2025). Optimal estimation of the canonical polyadic decomposition from low-rank tensor trains. Signal Processing. 234. 110001–110001.
2.
Dobigeon, Nicolas, et al.. (2024). Plug-and-Play Split Gibbs Sampler: Embedding Deep Generative Priors in Bayesian Inference. IEEE Transactions on Image Processing. 33. 3496–3507. 4 indexed citations
3.
Dobigeon, Nicolas, et al.. (2024). Normalizing flow sampling with Langevin dynamics in the latent space. Machine Learning. 113(11-12). 8301–8326. 1 indexed citations
4.
Chainais, Pierre, et al.. (2024). Denoising Bivariate Signals via Smoothing and Polarization Priors. SPIRE - Sciences Po Institutional REpository. 2602–2606.
5.
Paul, Sébastien, et al.. (2024). Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems. Computers & Chemical Engineering. 189. 108779–108779. 2 indexed citations
6.
Miron, Sébastian, et al.. (2023). Quaternions in Signal and Image Processing: A comprehensive and objective overview. IEEE Signal Processing Magazine. 40(6). 26–40. 27 indexed citations
7.
Chainais, Pierre, et al.. (2023). Efficient Sampling of Non Log-Concave Posterior Distributions With Mixture of Noises. IEEE Transactions on Signal Processing. 71. 2491–2501. 1 indexed citations
8.
Petit, Franck, Émeric Bron, Pierre Chainais, et al.. (2023). Neural network-based emulation of interstellar medium models. Astronomy and Astrophysics. 678. A198–A198. 5 indexed citations
9.
Dobigeon, Nicolas, et al.. (2022). High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm. SIAM Review. 64(1). 3–56. 16 indexed citations
10.
Dobigeon, Nicolas, et al.. (2019). Split-and-Augmented Gibbs Sampler—Application to Large-Scale Inference Problems. IEEE Transactions on Signal Processing. 67(6). 1648–1661. 25 indexed citations
11.
Chainais, Pierre, et al.. (2018). A Complete Framework for Linear Filtering of Bivariate Signals. IEEE Transactions on Signal Processing. 66(17). 4541–4552. 10 indexed citations
12.
Chainais, Pierre, et al.. (2017). Indian Buffet Process dictionary learning: Algorithms and applications to image processing. International Journal of Approximate Reasoning. 83. 1–20. 4 indexed citations
13.
Chainais, Pierre. (2013). Learning a common dictionary over a sensor network. HAL (Le Centre pour la Communication Scientifique Directe). 4 indexed citations
14.
Lebental, Bérengère, et al.. (2011). Aligned carbon nanotube based ultrasonic microtransducers for durability monitoring in civil engineering. Nanotechnology. 22(39). 395501–395501. 6 indexed citations
15.
Abry, Patrice, Pierre Chainais, Laure Coutin, & Vladas Pipiras. (2009). Multifractal Random Walks as Fractional Wiener Integrals. IEEE Transactions on Information Theory. 55(8). 3825–3846. 13 indexed citations
16.
Delouille, Véronique, Pierre Chainais, & J.‐F. Hochedez. (2008). Quantifying and containing the curse of high resolution coronal imaging. Annales Geophysicae. 26(10). 3169–3184. 2 indexed citations
17.
Chainais, Pierre. (2007). Infinitely Divisible Cascades to Model the Statistics of Natural Images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(12). 2105–2119. 26 indexed citations
18.
Chainais, Pierre, et al.. (2006). Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system. The European Symposium on Artificial Neural Networks. 659–664. 1 indexed citations
19.
Chainais, Pierre, Rudolf H. Riedi, & Patrice Abry. (2005). Warped infinitely divisible cascades: beyond power laws. Traitement du signal. 22(1). 27–39. 9 indexed citations
20.
Chainais, Pierre, Patrice Abry, & Darryl Veitch. (2002). Multifractal analysis and α-stable processes: a methodological contribution. 1. 241–244. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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