Gabriel Ilharco
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Aerospace Engineering
- Information Systems
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Mitchell WortsmanLudwig SchmidtJenia JitsevChristoph SchuhmannRomain BeaumontRoss WightmanMehdi ChertiHannaneh Hajishirzi
- Topics
- Multimodal Machine Learning Applications (9 papers)Domain Adaptation and Few-Shot Learning (7 papers)Topic Modeling (7 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/CVF International Conference on Computer Vision (ICCV)arXiv (Cornell University)
- Partner nations
- United StatesAustriaIsrael
In The Last Decade
Gabriel Ilharco
12 papers receiving 526 citations
Hit Papers
Peers
Comparison fields: 5 of 78
- Computer Vision and Pattern Recognition 354
- Artificial Intelligence 318
- Aerospace Engineering 31
- Information Systems 25
- Radiology, Nuclear Medicine and Imaging 25
Countries citing papers authored by Gabriel Ilharco
This map shows the geographic impact of Gabriel Ilharco'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 Gabriel Ilharco with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriel Ilharco more than expected).
Fields of papers citing papers by Gabriel Ilharco
This network shows the impact of papers produced by Gabriel Ilharco. 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 Gabriel Ilharco. The network helps show where Gabriel Ilharco may publish in the future.
Co-authorship network of co-authors of Gabriel Ilharco
This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel Ilharco. A scholar is included among the top collaborators of Gabriel Ilharco 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 Gabriel Ilharco. Gabriel Ilharco is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 66 | |
| 3 | Reproducible Scaling Laws for Contrastive Language-Image Learningbreakdown → | 213 |
| 4 | 8 | |
| 5 | 6 | |
| 6 | Robust fine-tuning of zero-shot modelsbreakdown → | 208 |
| 7 | 5 | |
| 8 | 1 | |
| 9 | 19 | |
| 10 | Probing Text Models for Common Ground with Visual Representations | 5 |
| 11 | 2 | |
| 12 | 19 |
About Gabriel Ilharco
Gabriel Ilharco is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 12 papers that have together received 553 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (9 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Topic Modeling (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (354 citations), Artificial Intelligence (318 citations) and Health Informatics (6 citations). Gabriel Ilharco has collaborated with scholars based in United States, Austria and Israel. Frequent co-authors include Mitchell Wortsman, Ludwig Schmidt, Jenia Jitsev, Christoph Schuhmann, Romain Beaumont, Ross Wightman, Mehdi Cherti, Ludwig Schmidt, Hannaneh Hajishirzi and Ali Farhadi. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and arXiv (Cornell University).
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.