Ludovic Denoyer
- Artificial Intelligence top 1%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Signal Processing top 10%
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Guillaume LampleMarc’Aurelio RanzatoAlexis ConneauPatrick GallinariHervé JeǵouMyle OttSandeep SubramanianEric M. Smith
- Topics
- Natural Language Processing Techniques (8 papers)Topic Modeling (7 papers)Complex Network Analysis Techniques (5 papers)
In The Last Decade
Ludovic Denoyer
31 papers receiving 969 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 853
- Computer Vision and Pattern Recognition 263
- Information Systems 110
- Signal Processing 84
- Radiology, Nuclear Medicine and Imaging 78
Countries citing papers authored by Ludovic Denoyer
This map shows the geographic impact of Ludovic Denoyer'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 Ludovic Denoyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ludovic Denoyer more than expected).
Fields of papers citing papers by Ludovic Denoyer
This network shows the impact of papers produced by Ludovic Denoyer. 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 Ludovic Denoyer. The network helps show where Ludovic Denoyer may publish in the future.
Co-authorship network of co-authors of Ludovic Denoyer
This figure shows the co-authorship network connecting the top 25 collaborators of Ludovic Denoyer. A scholar is included among the top collaborators of Ludovic Denoyer 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 Ludovic Denoyer. Ludovic Denoyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 0 | |
| 6 | 16 | |
| 7 | 15 | |
| 8 | Multiple-Attribute Text Rewriting. | 101 |
| 9 | Word translation without parallel databreakdown → | 306 |
| 10 | Stochastic Adaptive Neural Architecture Search for Keyword Spotting | 16 |
| 11 | 252 | |
| 12 | 11 | |
| 13 | A Meta-Learning Approach to One-Step Active Learning | 1 |
| 14 | 36 | |
| 15 | 0 | |
| 16 | 8 | |
| 17 | 27 | |
| 18 | 53 | |
| 19 | 35 | |
| 20 | Un modèle de mixture de modèles génératifs pour les documents structurés multimédias | 1 |
About Ludovic Denoyer
Ludovic Denoyer is a scholar working on Computational Mathematics, Artificial Intelligence and Signal Processing, having authored 34 papers that have together received 1.1k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (8 papers), Topic Modeling (7 papers) and Complex Network Analysis Techniques (5 papers). The work is most often cited by research in Artificial Intelligence (853 citations), Computational Mathematics (9 citations) and Computer Vision and Pattern Recognition (263 citations). Ludovic Denoyer has collaborated with scholars based in France, Israel and China. Frequent co-authors include Guillaume Lample, Marc’Aurelio Ranzato, Alexis Conneau, Patrick Gallinari, Hervé Jeǵou, Myle Ott, Sandeep Subramanian, Eric M. Smith, Y-Lan Boureau and Alexandre Denoyer. Their work appears in journals such as Machine Learning, British Journal of Ophthalmology and Journal of Cataract & Refractive Surgery.
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.