Yiding Jiang
Impact in
-
- E-commerce and Technology Innovations
-
- Reinforcement Learning in Robotics
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
Papers in
-
- Adversarial Robustness in Machine Learning 2
- Reinforcement Learning in Robotics 2
- Machine Learning and Data Classification 1
- Evolutionary Algorithms and Applications 1
- Neural Networks and Applications 1
- Co-authors
- Jiajie Song (1 shared paper)Yongfeng Chen (1 shared paper)Xueli Ma (1 shared paper)Dilip Krishnan (3 shared papers)Hossein Mobahi (4 shared papers)Samy Bengio (4 shared papers)Shixiang Gu (1 shared paper)Kevin P. Murphy (1 shared paper)
- Journals
- Frontiers in Psychology (1 paper)Neural Information Processing Systems (1 paper)International Conference on Learning Representations (2 papers)
- Partner nations
- United StatesChinaTaiwan
In The Last Decade
Yiding Jiang
7 papers receiving 61 citations
Peers
Comparison fields: 5 of 35
- Business and International Management 15
- Artificial Intelligence 33
- Marketing 8
- Health Informatics 1
- Computer Vision and Pattern Recognition 14
Countries citing papers authored by Yiding Jiang
This map shows the geographic impact of Yiding Jiang'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 Yiding Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yiding Jiang more than expected).
Fields of papers citing papers by Yiding Jiang
This network shows the impact of papers produced by Yiding Jiang. 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 Yiding Jiang. The network helps show where Yiding Jiang may publish in the future.
Co-authors
The 23 scholars most cited alongside Yiding Jiang, 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 | 2022 | 26 | |
| 2 | 2019 | 15 | |
| 3 | Language as an Abstraction for Hierarchical Deep Reinforcement Learning | 2019 | 8 |
| 4 | Fantastic Generalization Measures and Where to Find Them | 2020 | 8 |
| 5 | Predicting the Generalization Gap in Deep Networks with Margin Distributions | 2018 | 7 |
| 6 | Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning | 2020 | 1 |
| 7 | A Margin-Based Measure of Generalization for Deep Networks | 2019 | 1 |
| 8 | 2023 | 0 | |
| 9 | 2022 | 0 | |
| 10 | 2023 | 0 |
About Yiding Jiang
Yiding Jiang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Business and International Management and Computational Mechanics, having authored 10 papers that have together received 66 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Reinforcement Learning in Robotics (2 papers), Machine Learning and Data Classification (1 paper), Evolutionary Algorithms and Applications (1 paper), Sparse and Compressive Sensing Techniques (1 paper), Robotic Mechanisms and Dynamics (1 paper), E-commerce and Technology Innovations (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Business and International Management (15 citations), Artificial Intelligence (33 citations), Marketing (8 citations), Health Informatics (1 citation) and Computer Vision and Pattern Recognition (14 citations). Yiding Jiang has collaborated with scholars based in United States, China and Taiwan. Frequent co-authors include Jiajie Song, Yongfeng Chen, Xueli Ma, Dilip Krishnan, Hossein Mobahi, Samy Bengio, Shixiang Gu, Kevin P. Murphy, David Wang and Jeffrey Mahler. Their work appears in journals such as Frontiers in Psychology, Neural Information Processing Systems and International Conference on Learning Representations.
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.