Patrick Gallinari

1.9k total citations
50 papers, 745 citations indexed

About

Patrick Gallinari is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Patrick Gallinari has authored 50 papers receiving a total of 745 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 8 papers in Statistical and Nonlinear Physics. Recurrent topics in Patrick Gallinari's work include Neural Networks and Applications (9 papers), Speech Recognition and Synthesis (7 papers) and Meteorological Phenomena and Simulations (6 papers). Patrick Gallinari is often cited by papers focused on Neural Networks and Applications (9 papers), Speech Recognition and Synthesis (7 papers) and Meteorological Phenomena and Simulations (6 papers). Patrick Gallinari collaborates with scholars based in France, Italy and China. Patrick Gallinari's co-authors include Emmanuel de Bézenac, Arthur Pajot, Françoise Fogelman Soulié, Thierry Artières, Sanparith Marukatat, Yuan Yin, Abdelhamid Mellouk, Nicolas Thome, Ludovic Denoyer and Alexandre Stegner and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Applied Energy.

In The Last Decade

Patrick Gallinari

49 papers receiving 698 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Patrick Gallinari France 14 331 178 103 65 62 50 745
Cheng Tan China 12 325 1.0× 250 1.4× 66 0.6× 39 0.6× 53 0.9× 41 1.0k
Lirong Wu China 13 425 1.3× 237 1.3× 75 0.7× 51 0.8× 62 1.0× 38 828
Siamak Mehrkanoon Belgium 20 289 0.9× 213 1.2× 116 1.1× 39 0.6× 49 0.8× 66 956
Zhangyang Gao China 10 287 0.9× 218 1.2× 52 0.5× 53 0.8× 54 0.9× 32 769
Fredrik Lindsten Sweden 19 637 1.9× 96 0.5× 48 0.5× 45 0.7× 63 1.0× 63 1.1k
Charles Truong France 8 180 0.5× 66 0.4× 31 0.3× 68 1.0× 112 1.8× 25 771
Gherardo Varando Spain 7 229 0.7× 78 0.4× 27 0.3× 70 1.1× 38 0.6× 25 653
Xiangguo Zhao China 13 335 1.0× 75 0.4× 33 0.3× 31 0.5× 39 0.6× 45 572
Fahed Abdallah France 17 433 1.3× 284 1.6× 19 0.2× 63 1.0× 64 1.0× 53 861

Countries citing papers authored by Patrick Gallinari

Since Specialization
Citations

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

Fields of papers citing papers by Patrick Gallinari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Patrick Gallinari. 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 Patrick Gallinari. The network helps show where Patrick Gallinari may publish in the future.

Co-authorship network of co-authors of Patrick Gallinari

This figure shows the co-authorship network connecting the top 25 collaborators of Patrick Gallinari. A scholar is included among the top collaborators of Patrick Gallinari 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 Patrick Gallinari. Patrick Gallinari 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.
Gastineau, Guillaume, et al.. (2024). Separation of Internal and Forced Variability of Climate Using a U‐Net. Journal of Advances in Modeling Earth Systems. 16(6). 4 indexed citations
2.
Beretta, Andrea, Riccardo Guidotti, Yuan Yin, et al.. (2024). Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification. Diagnostics. 14(7). 753–753. 7 indexed citations
3.
Gartrell, Mike, et al.. (2023). Evaluating the Generalization Property of Prefix-based Methods for Data-to-text Generation. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
4.
Pop, Mihaela, et al.. (2023). Simultaneous data assimilation and cardiac electrophysiology model correction using differentiable physics and deep learning. Interface Focus. 13(6). 20230043–20230043. 2 indexed citations
5.
Hemptinne, Jean-Charles de, et al.. (2022). PTFlash : A vectorized and parallel deep learning framework for two-phase flash calculation. Fuel. 331. 125603–125603. 9 indexed citations
6.
Yin, Yuan, et al.. (2021). Augmenting physical models with deep networks for complex dynamics forecasting*. Journal of Statistical Mechanics Theory and Experiment. 2021(12). 124012–124012. 59 indexed citations
7.
Cancelliere, R., et al.. (2021). The Rare Word Issue in Natural Language Generation: A Character-Based Solution. Informatics. 8(1). 20–20. 2 indexed citations
8.
Douzal-Chouakria, Ahlame, et al.. (2020). Interpretable time series kernel analytics by pre-image estimation. Artificial Intelligence. 286. 103342–103342. 3 indexed citations
9.
Stegner, Alexandre, et al.. (2020). Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13. 3437–3447. 11 indexed citations
10.
Pajot, Arthur, Emmanuel de Bézenac, & Patrick Gallinari. (2018). Unsupervised Adversarial Image Reconstruction. International Conference on Learning Representations. 13 indexed citations
11.
Bézenac, Emmanuel de, Arthur Pajot, & Patrick Gallinari. (2017). Deep Learning for Physical Processes: Incorporating Prior Scientific\n Knowledge. arXiv (Cornell University). 150 indexed citations
12.
Wang, Shengrui, et al.. (2016). Multiple Bayesian discriminant functions for high-dimensional massive data classification. Data Mining and Knowledge Discovery. 31(2). 465–501. 13 indexed citations
13.
Mesrob, Lilia, Marie Sarazin, Valérie Hahn‐Barma, et al.. (2012). DTI and Structural MRI Classification in Alzheimer’s Disease. 2(2). 12–20. 33 indexed citations
14.
Tsatsaronis, George, Michael Schroeder, Γεώργιος Παλιούρας, et al.. (2012). BioASQ: A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering.. 43 indexed citations
15.
Artières, Thierry, Sanparith Marukatat, & Patrick Gallinari. (2007). Online Handwritten Shape Recognition Using Segmental Hidden Markov Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(2). 205–217. 48 indexed citations
16.
Usunier, Nicolas, Massih-Reza Amini, & Patrick Gallinari. (2005). Generalization error bounds for classifiers trained with interdependent data. Neural Information Processing Systems. 18. 1369–1376. 14 indexed citations
17.
Artières, Thierry, et al.. (2004). STROKE LEVEL MODELING OF ON LINE HANDWRITING THROUGH MULTIMODAL SEGMENTAL MODEL. Data Archiving and Networked Services (DANS). 93–102. 4 indexed citations
18.
Usunier, Nicolas, Massih-Reza Amini, & Patrick Gallinari. (2004). Génération de requêtes pour les systèmes de Q/R avec un modèle d'apprentissage statistique. HAL (Le Centre pour la Communication Scientifique Directe). 1 indexed citations
19.
Ma, Lin, Haifeng Li, Jiqing Han, & Patrick Gallinari. (2003). Hidden control neural network and HMM hybrid approach for on-line cursive handwriting recognition. 236–239 Vol.1. 1 indexed citations
20.
Bennani, Younès, et al.. (1991). Validation of neural net architectures on speech recognition tasks. 97–100 vol.1. 5 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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