Greg Yang
Impact in
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- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
- Stochastic Gradient Optimization Techniques
- Gaussian Processes and Bayesian Inference
Papers in
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- Neural Networks and Applications 4
- Adversarial Robustness in Machine Learning 4
- Gaussian Processes and Bayesian Inference 2
- Stochastic Gradient Optimization Techniques 2
- Machine Learning and ELM 2
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- Advanced Neural Network Applications 4
- Co-authors
- Hadi Salman (3 shared papers)Samuel S. Schoenholz (3 shared papers)Huan Zhang (1 shared paper)Pengchuan Zhang (1 shared paper)Cho‐Jui Hsieh (1 shared paper)Tony Duan (1 shared paper)Ilya Razenshteyn (1 shared paper)Jerry Li (1 shared paper)
- Journals
- Proceedings of the VLDB Endowment (1 paper)Journal of Micro/Nanolithography MEMS and MOEMS (1 paper)Journal of High Energy Physics (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1 paper)Neural Information Processing Systems (5 papers)
- Partner nations
- United StatesUnited KingdomSouth Korea
In The Last Decade
Greg Yang
14 papers receiving 82 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 70
- Computational Mathematics 1
- Structural Biology 2
- Computer Vision and Pattern Recognition 26
- Statistical and Nonlinear Physics 11
Countries citing papers authored by Greg Yang
This map shows the geographic impact of Greg 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 Greg Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Greg Yang more than expected).
Fields of papers citing papers by Greg Yang
This network shows the impact of papers produced by Greg 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 Greg Yang. The network helps show where Greg Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Greg 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 | A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks | 2019 | 21 |
| 2 | Randomized Smoothing of All Shapes and Sizes | 2020 | 17 |
| 3 | Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes | 2019 | 15 |
| 4 | A Mean Field Theory of Batch Normalization | 2019 | 14 |
| 5 | Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics | 2021 | 5 |
| 6 | Mean Field Residual Networks: On the Edge of Chaos | 2017 | 5 |
| 7 | Denoised Smoothing: A Provable Defense for Pretrained Classifiers | 2020 | 3 |
| 8 | 2012 | 2 | |
| 9 | 2013 | 2 | |
| 10 | 2021 | 2 | |
| 11 | Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes | 2019 | 1 |
| 12 | 2022 | 1 | |
| 13 | Deep Mean Field Theory: Layerwise Variance and Width Variation as Methods to Control Gradient Explosion | 2018 | 1 |
| 14 | The Dynamics of Signal Propagation in Gated Recurrent Neural Networks | 2019 | 1 |
| 15 | 2025 | 0 |
About Greg Yang
Greg Yang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mathematics and Surfaces, Coatings and Films, having authored 15 papers that have together received 90 indexed citations. Recurring topics across this work include Neural Networks and Applications (4 papers), Adversarial Robustness in Machine Learning (4 papers), Advanced Neural Network Applications (4 papers), Gaussian Processes and Bayesian Inference (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Model Reduction and Neural Networks (2 papers), Advancements in Photolithography Techniques (2 papers) and Machine Learning and ELM (2 papers). The work is most often cited by research in Artificial Intelligence (70 citations), Computational Mathematics (1 citation), Structural Biology (2 citations), Computer Vision and Pattern Recognition (26 citations) and Statistical and Nonlinear Physics (11 citations). Greg Yang has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include Hadi Salman, Samuel S. Schoenholz, Huan Zhang, Pengchuan Zhang, Cho‐Jui Hsieh, Tony Duan, Ilya Razenshteyn, Jerry Li, Jascha Sohl‐Dickstein and Jeffrey Pennington. Their work appears in journals such as Proceedings of the VLDB Endowment, Journal of Micro/Nanolithography MEMS and MOEMS, Journal of High Energy Physics, Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE and Neural Information Processing Systems.
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.