Dipendra Misra
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Control and Systems Engineering top 10%
- Social Psychology
- Aerospace Engineering
- Co-authors
- Ashutosh SaxenaJaeyong SungKevin LeeYoav ArtziValts BlukisEyvind NiklassonAndrew BennettPercy Liang
- Topics
- Natural Language Processing Techniques (5 papers)Topic Modeling (4 papers)Multimodal Machine Learning Applications (4 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceControl and Systems Engineering
- Journals
- The International Journal of Robotics ResearcharXiv (Cornell University)International Conference on Machine Learning
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Dipendra Misra
10 papers receiving 307 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 250
- Computer Vision and Pattern Recognition 174
- Control and Systems Engineering 86
- Social Psychology 20
- Aerospace Engineering 11
Countries citing papers authored by Dipendra Misra
This map shows the geographic impact of Dipendra Misra'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 Dipendra Misra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dipendra Misra more than expected).
Fields of papers citing papers by Dipendra Misra
This network shows the impact of papers produced by Dipendra Misra. 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 Dipendra Misra. The network helps show where Dipendra Misra may publish in the future.
Co-authorship network of co-authors of Dipendra Misra
This figure shows the co-authorship network connecting the top 25 collaborators of Dipendra Misra. A scholar is included among the top collaborators of Dipendra Misra 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 Dipendra Misra. Dipendra Misra 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 | 1 | |
| 3 | Provable Rich Observation Reinforcement Learning with Combinatorial Latent States | 2 |
| 4 | Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning | 6 |
| 5 | 71 | |
| 6 | 16 | |
| 7 | 16 | |
| 8 | 18 | |
| 9 | 33 | |
| 10 | 124 | |
| 11 | 33 |
About Dipendra Misra
Dipendra Misra is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 11 papers that have together received 320 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (5 papers), Topic Modeling (4 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (174 citations), Artificial Intelligence (250 citations) and Control and Systems Engineering (86 citations). Dipendra Misra has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Ashutosh Saxena, Jaeyong Sung, Kevin Lee, Yoav Artzi, Valts Blukis, Eyvind Niklasson, Andrew Bennett, Percy Liang, Ming‐Wei Chang and Ross A. Knepper. Their work appears in journals such as The International Journal of Robotics Research, 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.