Nir Levine
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Artificial Intelligence top 2%
- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Adversarial Robustness in Machine Learning
- Privacy-Preserving Technologies in Data
Papers in
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- Advanced Neural Network Applications 2
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- Information Retrieval and Search Behavior 1
- Co-authors
- Mehrdad Farajtabar (2 shared papers)Seyed Iman Mirzadeh (2 shared papers)Hassan G Ghasemzadeh (2 shared papers)Ang Li (2 shared papers)Cosmin Păduraru (1 shared paper)Sven Gowal (1 shared paper)Gabriel Dulac-Arnold (1 shared paper)Daniel J. Mankowitz (1 shared paper)
- Journals
- Machine Learning (1 paper)International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (1 paper)AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Nir Levine
4 papers receiving 1.2k citations
Nir Levine's Hit Papers
Peers
Comparison fields: 5 of 82
- Computer Vision and Pattern Recognition 548
- Artificial Intelligence 524
- Computer Networks and Communications 64
- Industrial and Manufacturing Engineering 25
- Acoustics and Ultrasonics 3
Countries citing papers authored by Nir Levine
This map shows the geographic impact of Nir Levine'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 Nir Levine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nir Levine more than expected).
Fields of papers citing papers by Nir Levine
This network shows the impact of papers produced by Nir Levine. 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 Nir Levine. The network helps show where Nir Levine may publish in the future.
Co-authors
The 11 scholars most cited alongside Nir Levine, 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 | Improved Knowledge Distillation via Teacher Assistant Hit paper breakdown → | 2020 | 765 |
| 2 | Challenges of real-world reinforcement learning: definitions, benchmarks and analysis Hit paper breakdown → | 2021 | 425 |
| 3 | 2020 | 53 | |
| 4 | 2017 | 10 |
About Nir Levine
Nir Levine is a scholar working on Computer Vision and Pattern Recognition, Information Systems, Artificial Intelligence, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 4 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Reinforcement Learning in Robotics (1 paper) and Information Retrieval and Search Behavior (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (548 citations), Artificial Intelligence (524 citations), Computer Networks and Communications (64 citations), Industrial and Manufacturing Engineering (25 citations) and Acoustics and Ultrasonics (3 citations). Nir Levine has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Mehrdad Farajtabar, Seyed Iman Mirzadeh, Hassan G Ghasemzadeh, Ang Li, Cosmin Păduraru, Sven Gowal, Gabriel Dulac-Arnold, Daniel J. Mankowitz, Todd Hester and Haggai Roitman. Their work appears in journals such as Machine Learning, International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.