Alex Irpan
Impact in
- Control and Systems Engineering top 10%
- Robot Manipulation and Learning
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
- Domain Adaptation and Few-Shot Learning
Papers in
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- Adversarial Robustness in Machine Learning 2
- Reinforcement Learning in Robotics 2
- Speech and dialogue systems 1
- Machine Learning and Algorithms 1
- Natural Language Processing Techniques 1
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- Robot Manipulation and Learning 2
- Co-authors
- Julian Ibarz (2 shared papers)Sergey Levine (2 shared papers)Kanishka Rao (1 shared paper)Mohi Khansari (1 shared paper)C.J. Harris (1 shared paper)Dmitry Kalashnikov (2 shared papers)Deirdre Quillen (1 shared paper)Mrinal Kalakrishnan (1 shared paper)
- Journals
- Uncertainty in Artificial Intelligence (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Alex Irpan
5 papers receiving 166 citations
Peers
Comparison fields: 5 of 39
- Control and Systems Engineering 98
- Artificial Intelligence 105
- Computer Vision and Pattern Recognition 63
- Human-Computer Interaction 8
- Biomedical Engineering 43
Countries citing papers authored by Alex Irpan
This map shows the geographic impact of Alex Irpan'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 Alex Irpan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex Irpan more than expected).
Fields of papers citing papers by Alex Irpan
This network shows the impact of papers produced by Alex Irpan. 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 Alex Irpan. The network helps show where Alex Irpan may publish in the future.
Co-authors
The 19 scholars most cited alongside Alex Irpan, 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 | 2020 | 88 | |
| 2 | QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation | 2018 | 71 |
| 3 | Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors | 2018 | 13 |
| 4 | Noise Contrastive Priors for Functional Uncertainty | 2018 | 4 |
| 5 | 2025 | 1 |
About Alex Irpan
Alex Irpan is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Statistics, Probability and Uncertainty and Biomedical Engineering, having authored 5 papers that have together received 177 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Reinforcement Learning in Robotics (2 papers), Robot Manipulation and Learning (2 papers), Multimodal Machine Learning Applications (2 papers), Speech and dialogue systems (1 paper), Machine Learning and Algorithms (1 paper), Muscle activation and electromyography studies (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Control and Systems Engineering (98 citations), Artificial Intelligence (105 citations), Computer Vision and Pattern Recognition (63 citations), Human-Computer Interaction (8 citations) and Biomedical Engineering (43 citations). Alex Irpan has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Julian Ibarz, Sergey Levine, Kanishka Rao, Mohi Khansari, C.J. Harris, Dmitry Kalashnikov, Deirdre Quillen, Mrinal Kalakrishnan, Eric Jang and Alexander Herzog. Their work appears in journals such as Uncertainty in 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.