Benjamin Eysenbach
Impact in
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- Data Visualization and Analytics
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- Reinforcement Learning in Robotics
- Explainable Artificial Intelligence (XAI)
- Advanced Clustering Algorithms Research
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
Papers in
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- Reinforcement Learning in Robotics 4
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 1
- Topic Modeling 1
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- Data Visualization and Analytics 1
- Advanced Vision and Imaging 1
- Multimodal Machine Learning Applications 1
- Co-authors
- Bum Chul Kwon (1 shared paper)Walter F. Stewart (1 shared paper)Kenney Ng (1 shared paper)Adam Perer (1 shared paper)Sergey Levine (5 shared papers)Shixiang Gu (1 shared paper)Julian Ibarz (1 shared paper)Abhishek Gupta (2 shared papers)
- Journals
- IEEE Transactions on Visualization and Computer Graphics (1 paper)International Conference on Learning Representations (1 paper)MPG.PuRe (Max Planck Society) (1 paper)arXiv (Cornell University) (2 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Benjamin Eysenbach
6 papers receiving 132 citations
Peers
Comparison fields: 5 of 55
- Computer Vision and Pattern Recognition 78
- Artificial Intelligence 86
- Signal Processing 17
- Health Informatics 2
- Biophysics 6
Countries citing papers authored by Benjamin Eysenbach
This map shows the geographic impact of Benjamin Eysenbach'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 Benjamin Eysenbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Eysenbach more than expected).
Fields of papers citing papers by Benjamin Eysenbach
This network shows the impact of papers produced by Benjamin Eysenbach. 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 Benjamin Eysenbach. The network helps show where Benjamin Eysenbach may publish in the future.
Co-authors
The 20 scholars most cited alongside Benjamin Eysenbach, 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 | 2017 | 91 | |
| 2 | Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning | 2018 | 26 |
| 3 | Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings | 2018 | 6 |
| 4 | 2019 | 6 | |
| 5 | Learning To Reach Goals Without Reinforcement Learning | 2019 | 3 |
| 6 | Model-Based Visual Planning with Self-Supervised Functional Distances | 2021 | 1 |
About Benjamin Eysenbach
Benjamin Eysenbach is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Safety Research, Infectious Diseases and Organic Chemistry, having authored 6 papers that have together received 133 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (4 papers), Domain Adaptation and Few-Shot Learning (2 papers), Machine Learning and Data Classification (1 paper), Data Visualization and Analytics (1 paper), Advanced Vision and Imaging (1 paper), Multimodal Machine Learning Applications (1 paper), Ethics and Social Impacts of AI (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (78 citations), Artificial Intelligence (86 citations), Signal Processing (17 citations), Health Informatics (2 citations) and Biophysics (6 citations). Benjamin Eysenbach has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Bum Chul Kwon, Walter F. Stewart, Kenney Ng, Adam Perer, Sergey Levine, Shixiang Gu, Julian Ibarz, Abhishek Gupta, Pieter Abbeel and Kyle Hsu. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, International Conference on Learning Representations, MPG.PuRe (Max Planck Society), arXiv (Cornell University) and International Conference on Machine Learning.
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.