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.
This map shows the geographic impact of Been Kim'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 Been Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Been Kim more than expected).
This network shows the impact of papers produced by Been Kim. 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 Been Kim. The network helps show where Been Kim may publish in the future.
Co-authorship network of co-authors of Been Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Been Kim.
A scholar is included among the top collaborators of Been Kim 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 Been Kim. Been Kim is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bai, Xiao, Xiang Wang, Xianglong Liu, et al.. (2021). Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. Pattern Recognition. 120. 108102–108102.208 indexed citations breakdown →
6.
Reif, Emily, Ann Yuan, Martin Wattenberg, et al.. (2019). Visualizing and Measuring the Geometry of BERT. Neural Information Processing Systems. 32. 8592–8600.70 indexed citations
7.
Ghorbani, Amirata, James Wexler, & Been Kim. (2019). Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks.. arXiv (Cornell University).7 indexed citations
8.
Yang, Mengjiao & Been Kim. (2019). BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth.. arXiv (Cornell University).9 indexed citations
9.
Khanna, Rajiv, Been Kim, Joydeep Ghosh, & Sanmi Koyejo. (2019). Interpreting black box predictions using fisher kernels. International Conference on Artificial Intelligence and Statistics. 3382–3390.9 indexed citations
Jiang, Heinrich, Been Kim, Melody Y. Guan, & Maya R. Gupta. (2018). To Trust Or Not To Trust A Classifier. arXiv (Cornell University). 31. 5541–5552.35 indexed citations
12.
Adebayo, Julius, Justin Gilmer, Michael Muelly, et al.. (2018). Sanity Checks for Saliency Maps. arXiv (Cornell University). 31. 9505–9515.206 indexed citations
Kim, Been, Julie Shah, & Finale Doshi‐Velez. (2015). Mind the Gap: a generative approach to interpretable feature selection and extraction. Neural Information Processing Systems. 28. 2260–2268.53 indexed citations
Kim, Been, Cynthia Rudin, & Julie Shah. (2014). The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. DSpace@MIT (Massachusetts Institute of Technology). 27. 1952–1960.72 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.