Ben Eisner
Impact in
- Human-Computer Interaction top 5%
- Digital Communication and Language
- Artificial Intelligence top 10%
- Sentiment Analysis and Opinion Mining
- Topic Modeling
- Advanced Text Analysis Techniques
- Natural Language Processing Techniques
- Hate Speech and Cyberbullying Detection
Papers in
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- Receptor Mechanisms and Signaling 1
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- Human Motion and Animation 1
- Co-authors
- Matko Bošnjak (1 shared paper)Sebastian Riedel (1 shared paper)Tim Rocktäschel (1 shared paper)Isabelle Augenstein (1 shared paper)David Held (2 shared papers)Harry Zhang (1 shared paper)Kai Zhang (1 shared paper)Xingyu Lin (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)2022 International Conference on Robotics and Automation (ICRA) (1 paper)
- Partner nations
- United States
In The Last Decade
Ben Eisner
4 papers receiving 233 citations
Peers
Comparison fields: 5 of 40
- Human-Computer Interaction 73
- Artificial Intelligence 187
- Computer Vision and Pattern Recognition 29
- Experimental and Cognitive Psychology 17
- Control and Systems Engineering 29
Countries citing papers authored by Ben Eisner
This map shows the geographic impact of Ben Eisner'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 Ben Eisner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben Eisner more than expected).
Fields of papers citing papers by Ben Eisner
This network shows the impact of papers produced by Ben Eisner. 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 Ben Eisner. The network helps show where Ben Eisner may publish in the future.
Co-authors
The 11 scholars most cited alongside Ben Eisner, 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 | 2016 | 193 | |
| 2 | 2022 | 31 | |
| 3 | 2022 | 16 | |
| 4 | QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error | 2019 | 2 |
About Ben Eisner
Ben Eisner is a scholar working on Molecular Biology, Control and Systems Engineering, Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence, having authored 4 papers that have together received 242 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (1 paper), Sentiment Analysis and Opinion Mining (1 paper), Soft Robotics and Applications (1 paper), Receptor Mechanisms and Signaling (1 paper), Hate Speech and Cyberbullying Detection (1 paper), Neural dynamics and brain function (1 paper), Human Motion and Animation (1 paper) and Digital Communication and Language (1 paper). The work is most often cited by research in Human-Computer Interaction (73 citations), Artificial Intelligence (187 citations), Computer Vision and Pattern Recognition (29 citations), Experimental and Cognitive Psychology (17 citations) and Control and Systems Engineering (29 citations). Ben Eisner has collaborated with scholars based in United States. Frequent co-authors include Matko Bošnjak, Sebastian Riedel, Tim Rocktäschel, Isabelle Augenstein, David Held, Harry Zhang, Kai Zhang, Xingyu Lin, Daniel C. Lee and Sebastian Seung. Their work appears in journals such as arXiv (Cornell University) and 2022 International Conference on Robotics and Automation (ICRA).
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.