Gautier Izacard
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging
- Media Technology top 10%
- Electrical and Electronic Engineering
- Co-authors
- Édouard GraveArmand JoulinGabriel SynnaevePiotr BojanowskiMathilde CaronHervé JeǵouHugo TouvronAlaaeldin El-Nouby
- Topics
- Topic Modeling (3 papers)Multimodal Machine Learning Applications (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceNature Machine IntelligenceHAL (Le Centre pour la Communication Scientifique Directe)
- Partner nations
- FranceUnited KingdomUnited States
In The Last Decade
Gautier Izacard
6 papers receiving 484 citations
Hit Papers
Peers
Comparison fields: 5 of 84
- Computer Vision and Pattern Recognition 244
- Artificial Intelligence 220
- Radiology, Nuclear Medicine and Imaging 64
- Media Technology 44
- Electrical and Electronic Engineering 44
Countries citing papers authored by Gautier Izacard
This map shows the geographic impact of Gautier Izacard'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 Gautier Izacard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gautier Izacard more than expected).
Fields of papers citing papers by Gautier Izacard
This network shows the impact of papers produced by Gautier Izacard. 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 Gautier Izacard. The network helps show where Gautier Izacard may publish in the future.
Co-authorship network of co-authors of Gautier Izacard
This figure shows the co-authorship network connecting the top 25 collaborators of Gautier Izacard. A scholar is included among the top collaborators of Gautier Izacard 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 Gautier Izacard. Gautier Izacard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 21 | |
| 2 | 12 | |
| 3 | ResMLP: Feedforward Networks for Image Classification With Data-Efficient Trainingbreakdown → | 444 |
| 4 | Distilling Knowledge from Reader to Retriever for Question Answering | 1 |
| 5 | Lossless Data Compression with Transformer | 1 |
| 6 | 18 |
About Gautier Izacard
Gautier Izacard is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Communication, having authored 6 papers that have together received 497 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Multimodal Machine Learning Applications (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (244 citations), Artificial Intelligence (220 citations) and Health Informatics (7 citations). Gautier Izacard has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Édouard Grave, Armand Joulin, Gabriel Synnaeve, Piotr Bojanowski, Mathilde Caron, Hervé Jeǵou, Hugo Touvron, Alaaeldin El-Nouby, Jakob Verbeek and Matthieu Cord. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Nature Machine Intelligence and HAL (Le Centre pour la Communication Scientifique Directe).
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.