Eric Jang
Impact in
-
- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
-
- Robot Manipulation and Learning
Papers in
-
- Robot Manipulation and Learning 6
-
- Reinforcement Learning in Robotics 3
- Domain Adaptation and Few-Shot Learning 2
- Adversarial Robustness in Machine Learning 2
- Co-authors
- Sergey Levine (7 shared papers)Jasmine Hsu (1 shared paper)Pierre Sermanet (1 shared paper)Stefan Schaal (1 shared paper)Corey Lynch (1 shared paper)Yevgen Chebotar (1 shared paper)Alexander Toshev (1 shared paper)Shixiang Gu (1 shared paper)
- Journals
- Frontiers in Neural Circuits (1 paper)arXiv (Cornell University) (3 papers)MPG.PuRe (Max Planck Society) (1 paper)
- Partner nations
- United StatesAustriaGermany
In The Last Decade
Eric Jang
13 papers receiving 629 citations
Eric Jang's Hit Papers
Peers
Comparison fields: 5 of 76
- Computer Vision and Pattern Recognition 343
- Control and Systems Engineering 261
- Artificial Intelligence 355
- Human-Computer Interaction 30
- Media Technology 18
Countries citing papers authored by Eric Jang
This map shows the geographic impact of Eric Jang'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 Jang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Jang more than expected).
Fields of papers citing papers by Eric Jang
This network shows the impact of papers produced by Eric Jang. 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 Jang. The network helps show where Eric Jang may publish in the future.
Co-authors
The 25 scholars most cited alongside Eric Jang, 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 | Time-Contrastive Networks: Self-Supervised Learning from Video Hit paper breakdown → | 2018 | 348 |
| 2 | QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation | 2018 | 73 |
| 3 | 2018 | 66 | |
| 4 | 2021 | 52 | |
| 5 | Categorical Reparametrization with Gumble-Softmax | 2017 | 51 |
| 6 | Generative Ensembles for Robust Anomaly Detection | 2018 | 26 |
| 7 | End-to-End Learning of Semantic Grasping | 2017 | 13 |
| 8 | Grasp2Vec: Learning Object Representations from Self-Supervised Grasping. | 2018 | 12 |
| 9 | 2016 | 5 | |
| 10 | Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards | 2020 | 4 |
| 11 | Meta-Learning Requires Meta-Augmentation | 2020 | 4 |
| 12 | 2023 | 3 | |
| 13 | 2020 | 2 |
About Eric Jang
Eric Jang is a scholar working on Control and Systems Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology and Cellular and Molecular Neuroscience, having authored 13 papers that have together received 659 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (6 papers), Advanced Vision and Imaging (3 papers), Reinforcement Learning in Robotics (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Adversarial Robustness in Machine Learning (2 papers), Human Pose and Action Recognition (2 papers), Multimodal Machine Learning Applications (2 papers) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (343 citations), Control and Systems Engineering (261 citations), Artificial Intelligence (355 citations), Human-Computer Interaction (30 citations) and Media Technology (18 citations). Eric Jang has collaborated with scholars based in United States, Austria and Germany. Frequent co-authors include Sergey Levine, Jasmine Hsu, Pierre Sermanet, Stefan Schaal, Corey Lynch, Yevgen Chebotar, Alexander Toshev, Shixiang Gu, Fereshteh Sadeghi and Ben Poole. Their work appears in journals such as Frontiers in Neural Circuits, arXiv (Cornell University) and MPG.PuRe (Max Planck Society).
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.