Cijo Jose
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Aerospace Engineering
- Automotive Engineering
- Control and Systems Engineering
- Co-authors
- François FleuretManik VarmaPrasoon GoyalPascal FuaLuc Van GoolPierre BaquéTatjana ChavdarovaAndrii Maksai
- Topics
- Advanced Image and Video Retrieval Techniques (2 papers)Anomaly Detection Techniques and Applications (2 papers)Image Retrieval and Classification Techniques (1 paper)
- Journals
- Infoscience (Ecole Polytechnique Fédérale de Lausanne)International Conference on Machine Learning
- Partner nations
- SwitzerlandIndia
In The Last Decade
Cijo Jose
5 papers receiving 185 citations
Peers
Comparison fields: 5 of 48
- Computer Vision and Pattern Recognition 130
- Artificial Intelligence 77
- Aerospace Engineering 26
- Automotive Engineering 23
- Control and Systems Engineering 14
Countries citing papers authored by Cijo Jose
This map shows the geographic impact of Cijo Jose'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 Cijo Jose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cijo Jose more than expected).
Fields of papers citing papers by Cijo Jose
This network shows the impact of papers produced by Cijo Jose. 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 Cijo Jose. The network helps show where Cijo Jose may publish in the future.
Co-authorship network of co-authors of Cijo Jose
This figure shows the co-authorship network connecting the top 25 collaborators of Cijo Jose. A scholar is included among the top collaborators of Cijo Jose 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 Cijo Jose. Cijo Jose 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 | 121 | |
| 3 | Importance sampling tree for large-scale empirical expectation | 7 |
| 4 | Local Deep Kernel Learning for Efficient Non-linear SVM Prediction | 53 |
| 5 | Classification and Prediction of Wind Tunnel Mach Number Responses Using Both Competitive and Gamma Neural Networks | 6 |
About Cijo Jose
Cijo Jose is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 5 papers that have together received 188 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Image Retrieval and Classification Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (130 citations), Artificial Intelligence (77 citations) and Automotive Engineering (23 citations). Cijo Jose has collaborated with scholars based in Switzerland and India. Frequent co-authors include François Fleuret, Manik Varma, Prasoon Goyal, Pascal Fua, Luc Van Gool, Pierre Baqué, Tatjana Chavdarova, Andrii Maksai, Timur Bagautdinov and Timothée Darcet. Their work appears in journals such as Infoscience (Ecole Polytechnique Fédérale de Lausanne) and International Conference on Machine Learning.
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.