Greg Yang
Impact in
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning
- Neural Networks and Applications
- Gaussian Processes and Bayesian Inference
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
- Stochastic Gradient Optimization Techniques
Papers in
-
- Adversarial Robustness in Machine Learning 6
- Neural Networks and Applications 4
- Gaussian Processes and Bayesian Inference 3
- Computational Physics and Python Applications 2
-
- Advanced Neural Network Applications 4
- Co-authors
- Hadi Salman (4 shared papers)Ilya Razenshteyn (2 shared papers)Jeffrey Pennington (3 shared papers)J. Edward Hu (1 shared paper)Huan Zhang (2 shared papers)Pengchuan Zhang (2 shared papers)Jascha Sohl‐Dickstein (2 shared papers)Samuel S. Schoenholz (3 shared papers)
- Journals
- Journal of Micro/Nanolithography MEMS and MOEMS (1 paper)Journal of High Energy Physics (1 paper)International Conference on Machine Learning (1 paper)arXiv (Cornell University) (2 papers)Neural Information Processing Systems (5 papers)
- Partner nations
- United StatesUnited KingdomSouth Korea
In The Last Decade
Greg Yang
16 papers receiving 150 citations
Peers
Comparison fields: 5 of 47
- Computational Mathematics 3
- Artificial Intelligence 133
- Computer Vision and Pattern Recognition 46
- Statistical and Nonlinear Physics 20
- Structural Biology 2
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 | 2018 | 38 | |
| 2 | Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers | 2019 | 29 |
| 3 | A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks | 2019 | 16 |
| 4 | Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes | 2019 | 16 |
| 5 | Randomized Smoothing of All Shapes and Sizes | 2020 | 16 |
| 6 | A Mean Field Theory of Batch Normalization | 2019 | 13 |
| 7 | Tensor Programs IV: Feature Learning in Infinite-Width Neural Networks | 2021 | 13 |
| 8 | Mean Field Residual Networks: On the Edge of Chaos | 2017 | 6 |
| 9 | Tensor Programs IIb: Architectural Universality Of Neural Tangent Kernel Training Dynamics | 2021 | 5 |
| 10 | Denoised Smoothing: A Provable Defense for Pretrained Classifiers | 2020 | 2 |
| 11 | 2021 | 2 | |
| 12 | 2012 | 2 | |
| 13 | 2013 | 2 | |
| 14 | Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes | 2019 | 1 |
| 15 | Deep Mean Field Theory: Layerwise Variance and Width Variation as Methods to Control Gradient Explosion | 2018 | 1 |
| 16 | The Dynamics of Signal Propagation in Gated Recurrent Neural Networks | 2019 | 1 |
| 17 | 2022 | 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 Electrical and Electronic Engineering, having authored 17 papers that have together received 163 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (6 papers), Neural Networks and Applications (4 papers), Advanced Neural Network Applications (4 papers), Gaussian Processes and Bayesian Inference (3 papers), Computational Physics and Python Applications (2 papers), Tensor decomposition and applications (2 papers), Model Reduction and Neural Networks (2 papers) and Advancements in Photolithography Techniques (2 papers). The work is most often cited by research in Computational Mathematics (3 citations), Artificial Intelligence (133 citations), Computer Vision and Pattern Recognition (46 citations), Statistical and Nonlinear Physics (20 citations) and Structural Biology (2 citations). Greg Yang has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include Hadi Salman, Ilya Razenshteyn, Jeffrey Pennington, J. Edward Hu, Huan Zhang, Pengchuan Zhang, Jascha Sohl‐Dickstein, Samuel S. Schoenholz, Lechao Xiao and Jaehoon Lee. Their work appears in journals such as Journal of Micro/Nanolithography MEMS and MOEMS, Journal of High Energy Physics, International Conference on Machine Learning, arXiv (Cornell University) 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.