Adam Fisch
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 5%
- Molecular Biology
- Sociology and Political Science
- Topics
- Topic Modeling (6 papers)Natural Language Processing Techniques (5 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language ProcessingProceedings of the AAAI Conference on Artificial IntelligenceOxford University Press eBooks
- Partner nations
- United StatesIsrael
In The Last Decade
Adam Fisch
9 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 97
- Artificial Intelligence 1.7k
- Computer Vision and Pattern Recognition 554
- Information Systems 254
- Molecular Biology 73
- Sociology and Political Science 43
Countries citing papers authored by Adam Fisch
This map shows the geographic impact of Adam Fisch'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 Adam Fisch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam Fisch more than expected).
Fields of papers citing papers by Adam Fisch
This network shows the impact of papers produced by Adam Fisch. 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 Adam Fisch. The network helps show where Adam Fisch may publish in the future.
Co-authorship network of co-authors of Adam Fisch
This figure shows the co-authorship network connecting the top 25 collaborators of Adam Fisch. A scholar is included among the top collaborators of Adam Fisch 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 Adam Fisch. Adam Fisch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 22 | |
| 3 | Making Pre-trained Language Models Better Few-shot Learnersbreakdown → | 785 |
| 4 | 7 | |
| 5 | 115 | |
| 6 | 3 | |
| 7 | 71 | |
| 8 | Reading Wikipedia to Answer Open-Domain Questionsbreakdown → | 803 |
| 9 | 0 | |
| 10 | 2 | |
| 11 | 5 |
About Adam Fisch
Adam Fisch is a scholar working on General Social Sciences, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 1.8k indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Artificial Intelligence (1.7k citations), Computer Vision and Pattern Recognition (554 citations) and Information Systems (254 citations). Adam Fisch has collaborated with scholars based in United States and Israel. Frequent co-authors include Danqi Chen, Tianyu Gao, Antoine Bordes, Jason Weston, Robin Jia, Eunsol Choi, Minjoon Seo, Alon Talmor, Ledell Wu and Sumit Chopra. Their work appears in journals such as Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings of the AAAI Conference on Artificial Intelligence and Oxford University Press eBooks.
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.