Emmanuel de Bézenac

672 total citations
8 papers, 278 citations indexed

About

Emmanuel de Bézenac is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Atmospheric Science. According to data from OpenAlex, Emmanuel de Bézenac has authored 8 papers receiving a total of 278 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Statistical and Nonlinear Physics and 3 papers in Atmospheric Science. Recurrent topics in Emmanuel de Bézenac's work include Model Reduction and Neural Networks (4 papers), Meteorological Phenomena and Simulations (3 papers) and Gaussian Processes and Bayesian Inference (2 papers). Emmanuel de Bézenac is often cited by papers focused on Model Reduction and Neural Networks (4 papers), Meteorological Phenomena and Simulations (3 papers) and Gaussian Processes and Bayesian Inference (2 papers). Emmanuel de Bézenac collaborates with scholars based in France, Germany and United States. Emmanuel de Bézenac's co-authors include Patrick Gallinari, Arthur Pajot, Nicolas Thome, Yuan Yin, Syama Sundar Rangapuram, Lorenzo Stella, Konstantinos Benidis, Tim Januschowski, Michael Bohlke‐Schneider and Patrick Gallinari and has published in prestigious journals such as Machine Learning, Journal of Statistical Mechanics Theory and Experiment and arXiv (Cornell University).

In The Last Decade

Emmanuel de Bézenac

8 papers receiving 268 citations

Peers

Countries citing papers authored by Emmanuel de Bézenac

Since Specialization

Citations

This map shows the geographic impact of Emmanuel de Bézenac'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 Emmanuel de Bézenac with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emmanuel de Bézenac more than expected).

Fields of papers citing papers by Emmanuel de Bézenac

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Emmanuel de Bézenac. 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 Emmanuel de Bézenac. The network helps show where Emmanuel de Bézenac may publish in the future.

Co-authorship network of co-authors of Emmanuel de Bézenac

This figure shows the co-authorship network connecting the top 25 collaborators of Emmanuel de Bézenac. A scholar is included among the top collaborators of Emmanuel de Bézenac 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 Emmanuel de Bézenac. Emmanuel de Bézenac is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

#	Work	Indexed citations
1	Modelling spatiotemporal dynamics from Earth observation data with neural differential equations 2022 ·Machine Learning ·(unknown), Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari	2
2	Augmenting physical models with deep networks for complex dynamics forecasting* 2021 ·Journal of Statistical Mechanics Theory and Experiment ·Yuan Yin, (unknown), (unknown), Emmanuel de Bézenac, (unknown), Nicolas Thome, Patrick Gallinari	59
3	Normalizing Kalman Filters for Multivariate Time Series Analysis 2020 ·Neural Information Processing Systems ·Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke‐Schneider, (unknown), Lorenzo Stella, (unknown), Patrick Gallinari, Tim Januschowski	44
4	Deep Rao-Blackwellised Particle Filters for Time Series Forecasting 2020 ·Neural Information Processing Systems ·(unknown), Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus	8
5	Learning the Spatio-Temporal Dynamics of Physical Processes from Partial Observations 2020 ·(unknown), Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari	1
6	Unsupervised Adversarial Image Reconstruction 2018 ·International Conference on Learning Representations ·Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari	13
7	Learning Partially Observed PDE Dynamics with Neural Networks 2018 ·(unknown), Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari	1
8	Deep Learning for Physical Processes: Incorporating Prior Scientific\n Knowledge 2017 ·arXiv (Cornell University) ·Emmanuel de Bézenac, Arthur Pajot, Patrick Gallinari	150

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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