Eric Mitchell
Impact in
-
- Artificial Intelligence in Healthcare and Education
Papers in
-
- Topic Modeling 4
- Natural Language Processing Techniques 3
- Domain Adaptation and Few-Shot Learning 2
- Adversarial Robustness in Machine Learning 1
-
- Multimodal Machine Learning Applications 2
- Advanced Vision and Imaging 2
- Co-authors
- Chelsea Finn (7 shared papers)Christopher D. Manning (5 shared papers)Rafael Rafailov (1 shared paper)Huaxiu Yao (1 shared paper)Archit Sharma (2 shared papers)Dan Jurafsky (1 shared paper)Ananth Agarwal (1 shared paper)Peter Henderson (1 shared paper)
- Journals
- Nature Communications (1 paper)Transportation Research Record Journal of the Transportation Research Board (1 paper)National Conference on Artificial Intelligence (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesSouth Korea
In The Last Decade
Eric Mitchell
10 papers receiving 88 citations
Peers
Comparison fields: 5 of 42
- Health Informatics 7
- Structural Biology 4
- Artificial Intelligence 52
- Computer Science Applications 5
- Biophysics 4
Countries citing papers authored by Eric Mitchell
This map shows the geographic impact of Eric Mitchell'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 Eric Mitchell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Mitchell more than expected).
Fields of papers citing papers by Eric Mitchell
This network shows the impact of papers produced by Eric Mitchell. 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 Eric Mitchell. The network helps show where Eric Mitchell may publish in the future.
Co-authors
The 25 scholars most cited alongside Eric Mitchell, 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 | 2023 | 45 | |
| 2 | 2022 | 13 | |
| 3 | 2024 | 10 | |
| 4 | 2023 | 9 | |
| 5 | 2023 | 4 | |
| 6 | 2024 | 4 | |
| 7 | QXplore: Q-Learning Exploration by Maximizing Temporal Difference Error | 2019 | 2 |
| 8 | Higher-Order Function Networks for Learning Composable 3D Object Representations | 2020 | 2 |
| 9 | 1996 | 2 | |
| 10 | Challenges of Acquiring Compositional Inductive Biases via Meta-Learning | 2021 | 1 |
| 11 | 2021 | 1 | |
| 12 | 2024 | 0 |
About Eric Mitchell
Eric Mitchell is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Molecular Biology and Automotive Engineering, having authored 12 papers that have together received 93 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Multimodal Machine Learning Applications (2 papers), Advanced Vision and Imaging (2 papers), 3D Shape Modeling and Analysis (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Transportation and Mobility Innovations (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (7 citations), Structural Biology (4 citations), Artificial Intelligence (52 citations), Computer Science Applications (5 citations) and Biophysics (4 citations). Eric Mitchell has collaborated with scholars based in United States and South Korea. Frequent co-authors include Chelsea Finn, Christopher D. Manning, Rafael Rafailov, Huaxiu Yao, Archit Sharma, Dan Jurafsky, Ananth Agarwal, Peter Henderson, Patrick Liu and Will J. Armstrong. Their work appears in journals such as Nature Communications, Transportation Research Record Journal of the Transportation Research Board, National Conference on Artificial Intelligence, Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).
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.