Yiding Jiang

648 total citations
7 papers, 64 citations indexed

About

Yiding Jiang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Yiding Jiang has authored 7 papers receiving a total of 64 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Control and Systems Engineering. Recurrent topics in Yiding Jiang's work include Adversarial Robustness in Machine Learning (2 papers), Sparse and Compressive Sensing Techniques (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). Yiding Jiang is often cited by papers focused on Adversarial Robustness in Machine Learning (2 papers), Sparse and Compressive Sensing Techniques (1 paper) and Stochastic Gradient Optimization Techniques (1 paper). Yiding Jiang collaborates with scholars based in United States, United Kingdom and China. Yiding Jiang's co-authors include Hossein Mobahi, Samy Bengio, Dilip Krishnan, Xueli Ma, Yongfeng Chen, Jeffrey Mahler, Jeffrey Ichnowski, Michael Danielczuk, Ken Goldberg and Behnam Neyshabur and has published in prestigious journals such as Frontiers in Psychology, Neural Information Processing Systems and International Conference on Learning Representations.

In The Last Decade

Yiding Jiang

7 papers receiving 59 citations

Peers

Yiding Jiang
Comparison fields: 5 of 35
  • Artificial Intelligence 33
  • Computer Vision and Pattern Recognition 14
  • Business and International Management 14
  • Control and Systems Engineering 13
  • Marketing 8
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Citations per field, relative to Yiding Jiang
Yiding Jiang · 1×
Citations per year, relative to Yiding Jiang
Yiding Jiang · 1×

Countries citing papers authored by Yiding Jiang

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Yiding Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Yiding Jiang. A scholar is included among the top collaborators of Yiding Jiang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yiding Jiang. Yiding Jiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
# Work Indexed citations
1 24
2
Fantastic Generalization Measures and Where to Find Them
8
3
Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning
1
4
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
8
5
A Margin-Based Measure of Generalization for Deep Networks
1
6 15
7
Predicting the Generalization Gap in Deep Networks with Margin Distributions
7

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.

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