Brandon Yang
Impact in
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- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
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- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
Papers in ⓘ
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- Domain Adaptation and Few-Shot Learning 2
- Topic Modeling 1
- Reinforcement Learning in Robotics 1
- Stochastic Gradient Optimization Techniques 1
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- Advanced Neural Network Applications 2
- Co-authors
- Gabriel Bender (2 shared papers)Quoc V. Le (2 shared papers)Jiquan Ngiam (2 shared papers)Immanuel Trummer (1 shared paper)Christopher Ré (1 shared paper)Avanika Narayan (1 shared paper)Ram S. Bhatta (1 shared paper)Mesfin Tsige (1 shared paper)
- Journals
- Chemical Physics Letters (1 paper)Proceedings of the VLDB Endowment (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United States
In The Last Decade
Brandon Yang
5 papers receiving 158 citations
Peers
Comparison fields: 5 of 60
- Computer Vision and Pattern Recognition 96
- Artificial Intelligence 61
- Media Technology 14
- Human-Computer Interaction 8
- Health Informatics 1
Countries citing papers authored by Brandon Yang
This map shows the geographic impact of Brandon Yang'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 Brandon Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brandon Yang more than expected).
Fields of papers citing papers by Brandon Yang
This network shows the impact of papers produced by Brandon Yang. 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 Brandon Yang. The network helps show where Brandon Yang may publish in the future.
Co-authors
The 13 scholars most cited alongside Brandon Yang, 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 | 2019 | 123 | |
| 2 | 2023 | 27 | |
| 3 | 2015 | 6 | |
| 4 | Sample-Efficient Deep RL with Generative Adversarial Tree Search. | 2018 | 3 |
| 5 | Soft Conditional Computation. | 2019 | 2 |
About Brandon Yang
Brandon Yang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Radiology, Nuclear Medicine and Imaging and Management Science and Operations Research, having authored 5 papers that have together received 161 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Topic Modeling (1 paper), Data Quality and Management (1 paper), COVID-19 diagnosis using AI (1 paper), Reinforcement Learning in Robotics (1 paper), Stochastic Gradient Optimization Techniques (1 paper) and Software System Performance and Reliability (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (96 citations), Artificial Intelligence (61 citations), Media Technology (14 citations), Human-Computer Interaction (8 citations) and Health Informatics (1 citation). Brandon Yang has collaborated with scholars based in United States. Frequent co-authors include Gabriel Bender, Quoc V. Le, Jiquan Ngiam, Immanuel Trummer, Christopher Ré, Avanika Narayan, Ram S. Bhatta, Mesfin Tsige, Kamyar Azizzadenesheli and Weitang Liu. Their work appears in journals such as Chemical Physics Letters, Proceedings of the VLDB Endowment 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.