Yoav Levine
Impact in
- Computational Mathematics top 10%
- Artificial Intelligence top 5%
- Topic Modeling
- Quantum Computing Algorithms and Architecture
- Natural Language Processing Techniques
- Quantum Information and Cryptography
- Neural Networks and Reservoir Computing
Papers in
-
- Topic Modeling 5
- Natural Language Processing Techniques 3
- Quantum Computing Algorithms and Architecture 3
- Explainable Artificial Intelligence (XAI) 1
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- Quantum many-body systems 4
- Co-authors
- Amnon ShashuaOr SharirNadav CohenGiuseppe CarleoYoav ShohamKevin Leyton‐BrownOri RamDor Muhlgay
- Journals
- Physical Review Letters (2 papers)Transactions of the Association for Computational Linguistics (1 paper)Physical review. B. (1 paper)International Conference on Machine Learning (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- IsraelUnited States
In The Last Decade
Yoav Levine
10 papers receiving 436 citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Computational Mathematics 15
- Artificial Intelligence 257
- Health Informatics 9
- Atomic and Molecular Physics, and Optics 181
- Condensed Matter Physics 44
Countries citing papers authored by Yoav Levine
This map shows the geographic impact of Yoav 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 Yoav Levine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yoav Levine more than expected).
Fields of papers citing papers by Yoav Levine
This network shows the impact of papers produced by Yoav 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 Yoav Levine. The network helps show where Yoav Levine may publish in the future.
Co-authorship network
The 14 scholars most cited alongside Yoav 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 | 2024 | 3 | |
| 2 | In-Context Retrieval-Augmented Language Models Hit paper breakdown → | 2023 | 151 |
| 3 | 2023 | 24 | |
| 4 | Which transformer architecture fits my data? A vocabulary bottleneck in self-attention | 2021 | 3 |
| 5 | Limits to Depth Efficiencies of Self-Attention | 2020 | 7 |
| 6 | 2020 | 133 | |
| 7 | 2019 | 113 | |
| 8 | Deep Learning and Quantum Physics : A Fundamental Bridge | 2017 | 1 |
| 9 | Benefits of Depth for Long-Term Memory of Recurrent Networks | 2017 | 7 |
| 10 | 2017 | 9 |
About Yoav Levine
Yoav Levine is a scholar working on Artificial Intelligence, Atomic and Molecular Physics, and Optics, Hardware and Architecture, Condensed Matter Physics and Statistical and Nonlinear Physics, having authored 10 papers that have together received 451 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Quantum many-body systems (4 papers), Natural Language Processing Techniques (3 papers), Quantum Computing Algorithms and Architecture (3 papers), Graphene research and applications (1 paper), Machine Learning in Materials Science (1 paper), Parallel Computing and Optimization Techniques (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Computational Mathematics (15 citations), Artificial Intelligence (257 citations), Health Informatics (9 citations), Atomic and Molecular Physics, and Optics (181 citations) and Condensed Matter Physics (44 citations). Yoav Levine has collaborated with scholars based in Israel and United States. Frequent co-authors include Amnon Shashua, Or Sharir, Nadav Cohen, Giuseppe Carleo, Yoav Shoham, Kevin Leyton‐Brown, Ori Ram, Dor Muhlgay, Itay Dalmedigos and Arbel Haim. Their work appears in journals such as Physical Review Letters, Transactions of the Association for Computational Linguistics, Physical review. B., International Conference on Machine Learning and arXiv (Cornell University).
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.