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.
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
2021503 citationsDan Hendrycks, Steven Basart et al.2021 IEEE/CVF International Conference on Computer Vision (ICCV)profile →
Natural Adversarial Examples
2021374 citationsDan Hendrycks, Kevin Zhao et al.profile →
This map shows the geographic impact of Dan Hendrycks'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 Dan Hendrycks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Hendrycks more than expected).
This network shows the impact of papers produced by Dan Hendrycks. 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 Dan Hendrycks. The network helps show where Dan Hendrycks may publish in the future.
Co-authorship network of co-authors of Dan Hendrycks
This figure shows the co-authorship network connecting the top 25 collaborators of Dan Hendrycks.
A scholar is included among the top collaborators of Dan Hendrycks 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 Dan Hendrycks. Dan Hendrycks is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
19 of 19 papers shown
1.
Park, Peter S., et al.. (2024). AI deception: A survey of examples, risks, and potential solutions. Patterns. 5(5). 100988–100988.51 indexed citations breakdown →
Hendrycks, Dan, Steven Basart, Norman Mu, et al.. (2021). The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 8320–8329.503 indexed citations breakdown →
Hendrycks, Dan, et al.. (2021). CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review. Neural Information Processing Systems.1 indexed citations
9.
Hendrycks, Dan, Kevin Zhao, Steven Basart, Jacob Steinhardt, & Dawn Song. (2021). Natural Adversarial Examples. 15257–15266.374 indexed citations breakdown →
10.
Hendrycks, Dan, Norman Mu, Ekin D. Cubuk, et al.. (2020). AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. International Conference on Learning Representations.61 indexed citations
11.
Hendrycks, Dan, Steven Basart, Mantas Mazeika, et al.. (2019). A Benchmark for Anomaly Segmentation.. arXiv (Cornell University).23 indexed citations
12.
Hendrycks, Dan, Mantas Mazeika, Saurav Kadavath, & Dawn Song. (2019). Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. Neural Information Processing Systems. 32. 15637–15648.77 indexed citations
Hendrycks, Dan & Kevin Gimpel. (2017). Early Methods for Detecting Adversarial Images. arXiv (Cornell University).47 indexed citations
17.
Hendrycks, Dan & Steven Basart. (2017). A Quantitative Measure of Generative Adversarial Network Distributions.2 indexed citations
18.
Hendrycks, Dan & Kevin Gimpel. (2016). Generalizing and Improving Weight Initialization.. arXiv (Cornell University).1 indexed citations
19.
Hendrycks, Dan & Kevin Gimpel. (2016). Visible Progress on Adversarial Images and a New Saliency Map..10 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.