Nir Levine
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 2%
- Domain Adaptation and Few-Shot Learning
- Reinforcement Learning in Robotics
- Anomaly Detection Techniques and Applications
- Topic Modeling
- Adversarial Robustness in Machine Learning
Papers in
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- Reinforcement Learning in Robotics 3
- Adversarial Robustness in Machine Learning 1
- Data Stream Mining Techniques 1
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- Optimization and Search Problems 1
- Co-authors
- Ang Li (1 shared paper)Akihiro Matsukawa (1 shared paper)Hassan Ghasemzadeh (1 shared paper)Mehrdad Farajtabar (1 shared paper)Seyed Iman Mirzadeh (1 shared paper)Daniel J. Mankowitz (3 shared papers)Jerry Li (1 shared paper)Cosmin Păduraru (1 shared paper)
- Journals
- Machine Learning (1 paper)Automatica (1 paper)Bell System Technical Journal (1 paper)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (2 papers)
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Nir Levine
8 papers receiving 1.1k citations
Nir Levine's Hit Papers
Peers
Comparison fields: 5 of 107
- Computer Vision and Pattern Recognition 423
- Artificial Intelligence 596
- Computational Mathematics 5
- Control and Systems Engineering 126
- Industrial and Manufacturing Engineering 44
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 25 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 | 693 |
| 2 | Challenges of real-world reinforcement learning: definitions, benchmarks and analysis Hit paper breakdown → | 2021 | 362 |
| 3 | 1971 | 15 | |
| 4 | 1961 | 14 | |
| 5 | 2017 | 9 | |
| 6 | Rotting Bandits | 2017 | 6 |
| 7 | Shallow Updates for Deep Reinforcement Learning | 2017 | 5 |
| 8 | Robust Reinforcement Learning for Continuous Control with Model Misspecification | 2020 | 1 |
About Nir Levine
Nir Levine is a scholar working on Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering, Information Systems and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 1.1k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), Web Data Mining and Analysis (1 paper), Adversarial Robustness in Machine Learning (1 paper), Brain Tumor Detection and Classification (1 paper), Optimization and Search Problems (1 paper), Data Stream Mining Techniques (1 paper), Statistical and numerical algorithms (1 paper) and Adaptive Dynamic Programming Control (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (423 citations), Artificial Intelligence (596 citations), Computational Mathematics (5 citations), Control and Systems Engineering (126 citations) and Industrial and Manufacturing Engineering (44 citations). Nir Levine has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Ang Li, Akihiro Matsukawa, Hassan Ghasemzadeh, Mehrdad Farajtabar, Seyed Iman Mirzadeh, Daniel J. Mankowitz, Jerry Li, Cosmin Păduraru, Sven Gowal and Todd Hester. Their work appears in journals such as Machine Learning, Automatica, Bell System Technical Journal, arXiv (Cornell University) and Neural Information Processing Systems.
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.