Ohad Rubin

489 citations
3 papers · 248 · 1 hit paper · h-index 2

Impact in

Papers in

Journals
Transactions of the Association for Computational Linguistics (1 paper)Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (1 paper)
Partner nations
IsraelUnited States

In The Last Decade

Ohad Rubin

3 papers receiving 241 citations

Ohad Rubin's Hit Papers

Learning To Retrieve Prompts for In-Context Learning 2022 · 201 citations
2010+1+2Years since publication50100150200

Peers

Ohad Rubin
Comparison fields: 5 of 40
  • Artificial Intelligence 211
  • Computer Vision and Pattern Recognition 60
  • Health Informatics 3
  • Software 6
  • Information Systems 35
Replace Scott Yih with:
Scott Yih United States
Jon Saad-Falcon United States
Jianshu Ji China
Avi Caciularu Israel
Haoxuan Che Hong Kong
Shuofei Qiao China
Lingyong Yan China
Jane Dwivedi-Yu United States
Tania Bedrax-Weiss United States
Ohad Rubin relative to Scott Yih United States Scott Yih's profile →
Citations per field
00.5×
Scott Yih · 1×
Citations per year

Countries citing papers authored by Ohad Rubin

Since Specialization
Citations

This map shows the geographic impact of Ohad Rubin'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 Ohad Rubin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ohad Rubin more than expected).

Fields of papers citing papers by Ohad Rubin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ohad Rubin. 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 Ohad Rubin. The network helps show where Ohad Rubin may publish in the future.

Co-authors

The 2 scholars most cited alongside Ohad Rubin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ohad Rubin Line = papers co-authored together Ohad Rubin links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1
Learning To Retrieve Prompts for In-Context Learning
Hit paper breakdown →
2022201
2 202146
3 20241

About Ohad Rubin

Ohad Rubin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Infectious Diseases, Organic Chemistry and Surgery, having authored 3 papers that have together received 248 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Natural Language Processing Techniques (3 papers), Multimodal Machine Learning Applications (2 papers) and Speech Recognition and Synthesis (1 paper). The work is most often cited by research in Artificial Intelligence (211 citations), Computer Vision and Pattern Recognition (60 citations), Health Informatics (3 citations), Software (6 citations) and Information Systems (35 citations). Ohad Rubin has collaborated with scholars based in Israel and United States. Frequent co-authors include Jonathan Berant and Jonathan Herzig. Their work appears in journals such as Transactions of the Association for Computational Linguistics and Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.

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.

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