Tianyu Pang

6.8k citations
22 papers · 863 indexed · 1 hit paper · h-index 10
Topics
Adversarial Robustness in Machine Learning (13 papers)Anomaly Detection Techniques and Applications (6 papers)Advanced Neural Network Applications (4 papers)

In The Last Decade

Tianyu Pang

19 papers receiving 841 citations

Hit Papers

Evading Defenses to Transferable Adversarial Examples by ...20192026202120232019100200300400

Peers

Tianyu Pang
Comparison fields: 5 of 73
  • Artificial Intelligence 713
  • Computer Vision and Pattern Recognition 319
  • Signal Processing 178
  • Molecular Biology 96
  • Electrical and Electronic Engineering 77
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Citations per field
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Citations per year

Countries citing papers authored by Tianyu Pang

Since Specialization
Citations

This map shows the geographic impact of Tianyu Pang'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 Tianyu Pang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tianyu Pang more than expected).

Fields of papers citing papers by Tianyu Pang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tianyu Pang. 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 Tianyu Pang. The network helps show where Tianyu Pang may publish in the future.

Co-authorship network of co-authors of Tianyu Pang

This figure shows the co-authorship network connecting the top 25 collaborators of Tianyu Pang. A scholar is included among the top collaborators of Tianyu Pang 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 Tianyu Pang. Tianyu Pang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 5
2 0
3 0
4 10
5 0
6 8
7 7
8 2
9 63
10
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
8
11
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
5
12
Boosting Adversarial Training with Hypersphere Embedding
12
13 133
14
Improving Black-box Adversarial Attacks with a Transfer-based Prior
24
15 44
16
Max-Mahalanobis Linear Discriminant Analysis Networks
11
17 2
18
Evading Defenses to Transferable Adversarial Examples by Mitigating Attention Shift
1
19
Discovering Adversarial Examples with Momentum
30
20
Robust Deep Learning via Reverse Cross-Entropy Training and Thresholding Test.
9

About Tianyu Pang

Tianyu Pang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 22 papers that have together received 863 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (13 papers), Anomaly Detection Techniques and Applications (6 papers) and Advanced Neural Network Applications (4 papers). The work is most often cited by research in Artificial Intelligence (713 citations), Signal Processing (178 citations) and Computer Vision and Pattern Recognition (319 citations). Tianyu Pang has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Yinpeng Dong, Jun Zhu, Hang Su, Xiao Yang, Zihao Xiao, Qian Fu, Jun Zhu, Xiao Yang, Hang Su and Yuefeng Chen. Their work appears in journals such as Journal of Chromatography A, International Journal of Computer Vision and Urban Rail Transit.

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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