Haim Dubossarsky
- Artificial Intelligence top 10%
- Cultural Studies top 1%
- Cognitive Neuroscience
- Experimental and Cognitive Psychology
- Statistical and Nonlinear Physics top 10%
- Co-authors
- Eitan GrossmanDaphna WeinshallThomas T. HillsSimon De DeyneNina TahmasebiDominik SchlechtwegSimon HengchenBarbara McGillivray
- Topics
- Topic Modeling (6 papers)Natural Language Processing Techniques (4 papers)Language and cultural evolution (3 papers)
- Journals
- Nature NeuroscienceDevelopmental PsychologyApollo (University of Cambridge)
- Partner nations
- United KingdomIsraelAustralia
In The Last Decade
Haim Dubossarsky
10 papers receiving 306 citations
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 199
- Cultural Studies 103
- Cognitive Neuroscience 71
- Experimental and Cognitive Psychology 42
- Statistical and Nonlinear Physics 41
Countries citing papers authored by Haim Dubossarsky
This map shows the geographic impact of Haim Dubossarsky'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 Haim Dubossarsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haim Dubossarsky more than expected).
Fields of papers citing papers by Haim Dubossarsky
This network shows the impact of papers produced by Haim Dubossarsky. 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 Haim Dubossarsky. The network helps show where Haim Dubossarsky may publish in the future.
Co-authorship network of co-authors of Haim Dubossarsky
This figure shows the co-authorship network connecting the top 25 collaborators of Haim Dubossarsky. A scholar is included among the top collaborators of Haim Dubossarsky based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Haim Dubossarsky. Haim Dubossarsky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 33 | |
| 5 | 3 | |
| 6 | There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training. | 3 |
| 7 | 95 | |
| 8 | 8 | |
| 9 | 69 | |
| 10 | 76 | |
| 11 | 13 | |
| 12 | A bottom up approach to category mapping and meaning change. | 22 |
About Haim Dubossarsky
Haim Dubossarsky is a scholar working on Cultural Studies, Artificial Intelligence and Physical and Theoretical Chemistry, having authored 12 papers that have together received 324 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers) and Language and cultural evolution (3 papers). The work is most often cited by research in Cultural Studies (103 citations), Artificial Intelligence (199 citations) and General Social Sciences (15 citations). Haim Dubossarsky has collaborated with scholars based in United Kingdom, Israel and Australia. Frequent co-authors include Eitan Grossman, Daphna Weinshall, Thomas T. Hills, Simon De Deyne, Nina Tahmasebi, Dominik Schlechtweg, Simon Hengchen, Barbara McGillivray, Timothy E.J. Behrens and Avital Hahamy. Their work appears in journals such as Nature Neuroscience, Developmental Psychology and Apollo (University of Cambridge).
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.