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.
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
20101.2k citationsNguyễn Xuân Vinh, James Bailey et al.profile →
Symmetric Cross Entropy for Robust Learning With Noisy Labels
2019555 citationsYisen Wang, Xingjun Ma et al.profile →
Information theoretic measures for clusterings comparison
2009535 citationsNguyễn Xuân Vinh, James Bailey et al.profile →
Understanding adversarial attacks on deep learning based medical image analysis systems
2020304 citationsXingjun Ma, Yisen Wang 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 James Bailey'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 James Bailey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Bailey more than expected).
This network shows the impact of papers produced by James Bailey. 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 James Bailey. The network helps show where James Bailey may publish in the future.
Co-authorship network of co-authors of James Bailey
This figure shows the co-authorship network connecting the top 25 collaborators of James Bailey.
A scholar is included among the top collaborators of James Bailey 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 James Bailey. James Bailey is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Yisen, Difan Zou, Jinfeng Yi, et al.. (2020). Improving Adversarial Robustness Requires Revisiting Misclassified Examples. International Conference on Learning Representations.153 indexed citations
10.
Wang, Yisen, Bo Dai, Lingkai Kong, et al.. (2018). Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. Uncertainty in Artificial Intelligence. 1. 83–92.3 indexed citations
Ma, Xingjun, Yisen Wang, Michael E. Houle, et al.. (2018). Dimensionality-Driven Learning with Noisy Labels. Own your potential (DEAKIN). 3355–3364.49 indexed citations
14.
Lederman, Reeva, et al.. (2017). Using a Traffic Light System to Provide Feedback to IS Masters Students. Journal of the Association for Information Systems.
15.
Hellicar, Andrew, et al.. (2017). Efficient orthogonal parametrisation of recurrent neural networks using householder reflections. Minerva Access (University of Melbourne). 70. 2401–2409.17 indexed citations
16.
Erfani, Sarah, Mahsa Baktashmotlagh, Masud Moshtaghi, et al.. (2016). Robust domain generalisation by enforcing distribution invariance. QUT ePrints (Queensland University of Technology). 1455–1461.11 indexed citations
17.
Seifi, Abbas, et al.. (2015). Generalized Modularity for Community Detection. Lecture notes in computer science. 9285.1 indexed citations
18.
Demyanov, Sergey, James Bailey, Kotagiri Ramamohanarao, & Christopher Leckie. (2012). AIC and BIC based approaches for SVM parameter value estimation with RBF kernels. Asian Conference on Machine Learning. 25. 97–112.7 indexed citations
Bailey, James, et al.. (2007). Are zero-suppressed binary decision diagrams good for mining frequent patterns in high dimensional datasets?. 70. 139–150.5 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.