Eric Wiewiora
Impact in
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- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Artificial Intelligence top 2%
- Reinforcement Learning in Robotics
- Evolutionary Algorithms and Applications
Papers in
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- Reinforcement Learning in Robotics 5
- Machine Learning and Algorithms 3
- Evolutionary Algorithms and Applications 2
- Bayesian Modeling and Causal Inference 2
- Domain Adaptation and Few-Shot Learning 1
- Artificial Intelligence in Games 1
- Co-authors
- Andrew Rabinovich (1 shared paper)Andrea Vedaldi (1 shared paper)Carolina Galleguillos (1 shared paper)Serge Belongie (1 shared paper)John Langford (1 shared paper)Alexander L. Strehl (1 shared paper)Michael L. Littman (1 shared paper)Lihong Li (1 shared paper)
- Journals
- Journal of Vision (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Eric Wiewiora
7 papers receiving 942 citations
Eric Wiewiora's Hit Papers
Peers
Comparison fields: 5 of 83
- Computer Vision and Pattern Recognition 411
- Artificial Intelligence 531
- Management Science and Operations Research 115
- Computational Theory and Mathematics 139
- Media Technology 61
Countries citing papers authored by Eric Wiewiora
This map shows the geographic impact of Eric Wiewiora'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 Eric Wiewiora with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Wiewiora more than expected).
Fields of papers citing papers by Eric Wiewiora
This network shows the impact of papers produced by Eric Wiewiora. 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 Eric Wiewiora. The network helps show where Eric Wiewiora may publish in the future.
Co-authors
The 19 scholars most cited alongside Eric Wiewiora, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Objects in Context Hit paper breakdown → | 2007 | 419 |
| 2 | 2009 | 258 | |
| 3 | 2006 | 204 | |
| 4 | Principled methods for advising reinforcement learning agents | 2003 | 84 |
| 5 | 2005 | 22 | |
| 6 | Modeling probability distributions with predictive state representations | 2007 | 12 |
| 7 | Ecient Exploration for Reinforcement Learning | 2004 | 3 |
| 8 | 2010 | 0 |
About Eric Wiewiora
Eric Wiewiora is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Media Technology and Neurology, having authored 8 papers that have together received 1.0k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (5 papers), Machine Learning and Algorithms (3 papers), Evolutionary Algorithms and Applications (2 papers), Bayesian Modeling and Causal Inference (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Visual perception and processing mechanisms (1 paper), Remote-Sensing Image Classification (1 paper) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (411 citations), Artificial Intelligence (531 citations), Management Science and Operations Research (115 citations), Computational Theory and Mathematics (139 citations) and Media Technology (61 citations). Eric Wiewiora has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Andrew Rabinovich, Andrea Vedaldi, Carolina Galleguillos, Serge Belongie, John Langford, Alexander L. Strehl, Michael L. Littman, Lihong Li, Csaba Szepesvári and Hamid Reza Maei. Their work appears in journals such as Journal of Vision 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.