Fernando De la Torre
- Computer Vision and Pattern Recognition top 0.5%
- Plant Science top 2%
- Artificial Intelligence top 2%
- Molecular Biology
- Computational Mechanics top 2%
- Co-authors
- Jeffrey F. CohnMichael J. BlackLászló A. JeniFrancisco M. CánovasM.J. BlackConcepción ÁvilaJessica K. HodginsRafael A. Cañas
- Topics
- Plant nutrient uptake and metabolism (14 papers)Face and Expression Recognition (10 papers)Plant Gene Expression Analysis (9 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceThe Plant CellPLANT PHYSIOLOGY
- Partner nations
- United StatesSpainPortugal
In The Last Decade
Fernando De la Torre
89 papers receiving 4.1k citations
Hit Papers
Peers
Comparison fields: 5 of 194
- Computer Vision and Pattern Recognition 1.6k
- Plant Science 902
- Artificial Intelligence 652
- Molecular Biology 623
- Computational Mechanics 477
Countries citing papers authored by Fernando De la Torre
This map shows the geographic impact of Fernando De la Torre'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 Fernando De la Torre with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando De la Torre more than expected).
Fields of papers citing papers by Fernando De la Torre
This network shows the impact of papers produced by Fernando De la Torre. 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 Fernando De la Torre. The network helps show where Fernando De la Torre may publish in the future.
Co-authorship network of co-authors of Fernando De la Torre
This figure shows the co-authorship network connecting the top 25 collaborators of Fernando De la Torre. A scholar is included among the top collaborators of Fernando De la Torre 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 Fernando De la Torre. Fernando De la Torre is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 30 | |
| 3 | 15 | |
| 4 | 40 | |
| 5 | 8 | |
| 6 | 95 | |
| 7 | 48 | |
| 8 | 44 | |
| 9 | 63 | |
| 10 | Maximum Margin Temporal Clustering | 40 |
| 11 | 24 | |
| 12 | Gaussian Processes Multiple Instance Learning | 27 |
| 13 | 58 | |
| 14 | 9 | |
| 15 | 16 | |
| 16 | 44 | |
| 17 | 18 | |
| 18 | 72 | |
| 19 | Faune d'acariens de la poussière domestique dans l'Ile de Tenerife | 5 |
| 20 | 17 |
About Fernando De la Torre
Fernando De la Torre is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Plant Science, having authored 90 papers that have together received 4.3k indexed citations. Recurring topics across this work include Plant nutrient uptake and metabolism (14 papers), Face and Expression Recognition (10 papers) and Plant Gene Expression Analysis (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.6k citations), Computational Mathematics (44 citations) and Signal Processing (471 citations). Fernando De la Torre has collaborated with scholars based in United States, Spain and Portugal. Frequent co-authors include Jeffrey F. Cohn, Michael J. Black, László A. Jeni, Francisco M. Cánovas, M.J. Black, Concepción Ávila, Jessica K. Hodgins, Rafael A. Cañas, Feng Zhou and Deyu Meng. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The Plant Cell and PLANT PHYSIOLOGY.
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.