Colin Wei
Impact in
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- Domain Adaptation and Few-Shot Learning
- Stochastic Gradient Optimization Techniques
- Machine Learning and Data Classification
- Neural Networks and Applications
- Topic Modeling
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- Advanced Neural Network Applications
Papers in
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- Machine Learning and ELM 3
- Neural Networks and Applications 3
- Machine Learning and Algorithms 2
- Domain Adaptation and Few-Shot Learning 2
- Machine Learning and Data Classification 1
- Stochastic Gradient Optimization Techniques 1
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- Advanced Neural Network Applications 3
- Co-authors
- Tengyu Ma (4 shared papers)Yining Chen (1 shared paper)Jason D. Lee (2 shared papers)Tengyu Ma (3 shared papers)Qiang Liu (1 shared paper)Yuanzhi Li (1 shared paper)Stefano Ermon (1 shared paper)
- Journals
- arXiv (Cornell University) (3 papers)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)Neural Information Processing Systems (2 papers)neural information processing systems (1 paper)
- Partner nations
- United States
In The Last Decade
Colin Wei
7 papers receiving 71 citations
Peers
Comparison fields: 5 of 27
- Artificial Intelligence 63
- Computer Vision and Pattern Recognition 28
- Statistical and Nonlinear Physics 8
- Statistics and Probability 4
- Computational Mechanics 8
Countries citing papers authored by Colin Wei
This map shows the geographic impact of Colin Wei'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 Colin Wei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Colin Wei more than expected).
Fields of papers citing papers by Colin Wei
This network shows the impact of papers produced by Colin Wei. 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 Colin Wei. The network helps show where Colin Wei may publish in the future.
Co-authors
The 8 scholars most cited alongside Colin Wei, 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 | Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data | 2020 | 32 |
| 2 | Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel | 2019 | 16 |
| 3 | On the Margin Theory of Feedforward Neural Networks | 2018 | 12 |
| 4 | Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks | 2019 | 6 |
| 5 | Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation | 2019 | 3 |
| 6 | Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin | 2020 | 2 |
| 7 | 2017 | 1 | |
| 8 | 2022 | 0 |
About Colin Wei
Colin Wei is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Statistical and Nonlinear Physics and Signal Processing, having authored 8 papers that have together received 72 indexed citations. Recurring topics across this work include Machine Learning and ELM (3 papers), Neural Networks and Applications (3 papers), Advanced Neural Network Applications (3 papers), Machine Learning and Algorithms (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Data Management and Algorithms (1 paper), Machine Learning and Data Classification (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (63 citations), Computer Vision and Pattern Recognition (28 citations), Statistical and Nonlinear Physics (8 citations), Statistics and Probability (4 citations) and Computational Mechanics (8 citations). Colin Wei has collaborated with scholars based in United States. Frequent co-authors include Tengyu Ma, Yining Chen, Jason D. Lee, Tengyu Ma, Qiang Liu, Qiang Liu, Yuanzhi Li and Stefano Ermon. Their work appears in journals such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence, Neural Information Processing Systems 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.