Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
2018357 citationsJi Gao, Jack Lanchantin et al.profile →
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
2020302 citationsJohn X. Morris, Eli Lifland et al.profile →
General Multi-label Image Classification with Transformers
2021212 citationsJack Lanchantin, Yanjun Qi et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Yanjun Qi'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 Yanjun Qi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yanjun Qi more than expected).
This network shows the impact of papers produced by Yanjun Qi. 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 Yanjun Qi. The network helps show where Yanjun Qi may publish in the future.
Co-authorship network of co-authors of Yanjun Qi
This figure shows the co-authorship network connecting the top 25 collaborators of Yanjun Qi.
A scholar is included among the top collaborators of Yanjun Qi 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 Yanjun Qi. Yanjun Qi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Morris, John X., Eli Lifland, Jin Yong Yoo, & Yanjun Qi. (2020). TextAttack: A Framework for Adversarial Attacks in Natural Language Processing. arXiv (Cornell University).24 indexed citations
8.
Morris, John X., Eli Lifland, Jin Yong Yoo, et al.. (2020). TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP. 119–126.302 indexed citations breakdown →
Wang, Beilun, et al.. (2018). A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models. International Conference on Machine Learning. 5148–5157.1 indexed citations
11.
Wang, Beilun, Ji Gao, & Yanjun Qi. (2017). A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Samples. International Conference on Learning Representations.8 indexed citations
12.
Gao, Ji, Beilun Wang, Zeming Lin, Weilin Xu, & Yanjun Qi. (2017). DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples. International Conference on Learning Representations.4 indexed citations
13.
Wang, Beilun, et al.. (2017). Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. International Conference on Artificial Intelligence and Statistics. 1691–1700.1 indexed citations
14.
Wang, Beilun, Ji Gao, & Yanjun Qi. (2017). A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. International Conference on Artificial Intelligence and Statistics. 1168–1177.2 indexed citations
15.
Gao, Ji, Beilun Wang, & Yanjun Qi. (2017). DeepMask: Masking DNN Models for robustness against adversarial samples.. arXiv (Cornell University).6 indexed citations
Wang, Beilun, Ji Gao, & Yanjun Qi. (2016). A Theoretical Framework for Robustness of (Deep) Classifiers Under Adversarial Noise.. arXiv (Cornell University).7 indexed citations
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.