Chris Ying
Impact in
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- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Stochastic Gradient Optimization Techniques
- Machine Learning and Data Classification
- Neural Networks and Applications
- Adversarial Robustness in Machine Learning
- Machine Learning and ELM
Papers in
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- 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 2
- Co-authors
- Samuel Smith (1 shared paper)Pieter-Jan Kindermans (1 shared paper)Quoc V. Le (1 shared paper)Kevin Murphy (1 shared paper)Eric Christiansen (1 shared paper)Esteban Real (1 shared paper)Frank Hutter (1 shared paper)Aaron Klein (1 shared paper)
- Journals
- Integrated ferroelectrics (2 papers)arXiv (Cornell University) (2 papers)Medical Entomology and Zoology (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Chris Ying
6 papers receiving 242 citations
Peers
Comparison fields: 5 of 65
- Computer Vision and Pattern Recognition 145
- Artificial Intelligence 189
- Hardware and Architecture 10
- Signal Processing 12
- Computer Science Applications 5
Countries citing papers authored by Chris Ying
This map shows the geographic impact of Chris Ying'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 Chris Ying with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Ying more than expected).
Fields of papers citing papers by Chris Ying
This network shows the impact of papers produced by Chris Ying. 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 Chris Ying. The network helps show where Chris Ying may publish in the future.
Co-authors
The 18 scholars most cited alongside Chris Ying, 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 | Don't decay the learning rate, increase the batch size | 2018 | 106 |
| 2 | 2019 | 99 | |
| 3 | 2019 | 45 | |
| 4 | Voices from the storm : the people of New Orleans on Hurricane Katrina and its aftermath | 2006 | 5 |
| 5 | 2003 | 2 | |
| 6 | 2001 | 1 |
About Chris Ying
Chris Ying is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Materials Chemistry and Sociology and Political Science, having authored 6 papers that have together received 258 indexed citations. Recurring topics across this work include Semiconductor materials and devices (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Ferroelectric and Piezoelectric Materials (1 paper), Software Engineering Research (1 paper), Machine Learning and Data Classification (1 paper), Electrowetting and Microfluidic Technologies (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (145 citations), Artificial Intelligence (189 citations), Hardware and Architecture (10 citations), Signal Processing (12 citations) and Computer Science Applications (5 citations). Chris Ying has collaborated with scholars based in United States and Germany. Frequent co-authors include Samuel Smith, Pieter-Jan Kindermans, Quoc V. Le, Kevin Murphy, Eric Christiansen, Esteban Real, Frank Hutter, Aaron Klein, Cho‐Jui Hsieh and Yang You. Their work appears in journals such as Integrated ferroelectrics, arXiv (Cornell University) and Medical Entomology and Zoology.
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.