Justin Gilmer

9.1k total citations · 1 hit paper
10 papers, 895 citations indexed

About

Justin Gilmer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Justin Gilmer has authored 10 papers receiving a total of 895 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Justin Gilmer's work include Adversarial Robustness in Machine Learning (4 papers), Neural Networks and Applications (3 papers) and Explainable Artificial Intelligence (XAI) (3 papers). Justin Gilmer is often cited by papers focused on Adversarial Robustness in Machine Learning (4 papers), Neural Networks and Applications (3 papers) and Explainable Artificial Intelligence (XAI) (3 papers). Justin Gilmer collaborates with scholars based in United States, United Kingdom and Poland. Justin Gilmer's co-authors include Dan Hendrycks, Norman Mu, Steven Basart, Saurav Kadavath, Fengqiu Wang, Samyak Parajuli, Dawn Song, Tyler Zhu, Jacob Steinhardt and Been Kim and has published in prestigious journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), arXiv (Cornell University) and International Conference on Machine Learning.

In The Last Decade

Justin Gilmer

10 papers receiving 843 citations

Hit Papers

align trajectories

What are hit papers?

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

2021 503 citations Dan Hendrycks, Steven Basart et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Justin Gilmer United States	7	667	442	73	35	30	10	895
Steven Basart United States	4	825 1.2×	588 1.3×	70 1.0×	12 0.3×	26 0.9×	5	1.1k
Hideki Nakayama Japan	16	650 1.0×	702 1.6×	234 3.2×	51 1.5×	46 1.5×	78	1.2k
Jingwen Ye China	12	259 0.4×	606 1.4×	40 0.5×	16 0.5×	30 1.0×	19	856
Mahdieh Soleymani Baghshah Iran	15	423 0.6×	323 0.7×	59 0.8×	10 0.3×	55 1.8×	52	676
Lingzhi Li China	12	390 0.6×	907 2.1×	52 0.7×	17 0.5×	96 3.2×	38	1.2k
David López-Paz Germany	13	497 0.7×	312 0.7×	64 0.9×	46 1.3×	37 1.2×	26	867
Chih–Chung Hsu Taiwan	15	274 0.4×	783 1.8×	47 0.6×	23 0.7×	48 1.6×	74	1.1k
Xu Yang China	17	567 0.9×	430 1.0×	68 0.9×	5 0.1×	19 0.6×	55	873
Changhee Han Japan	10	316 0.5×	325 0.7×	249 3.4×	49 1.4×	38 1.3×	27	762

Countries citing papers authored by Justin Gilmer

Since Specialization

Citations

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

Fields of papers citing papers by Justin Gilmer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Justin Gilmer

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Tang, Jiaxi, Yoel Drori, Maheswaran Sathiamoorthy, et al.. (2023). Improving Training Stability for Multitask Ranking Models in Recommender Systems. 4882–4893. 4 indexed citations

2.

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 →

3.

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

4.

Engstrom, Logan, Justin Gilmer, Gabriel Goh, et al.. (2019). A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features'. 4(8). 12 indexed citations

5.

Adebayo, Julius, Justin Gilmer, Michael Muelly, et al.. (2018). Sanity Checks for Saliency Maps. arXiv (Cornell University). 31. 9505–9515. 206 indexed citations

6.

Adebayo, Julius, Justin Gilmer, Ian Goodfellow, & Been Kim. (2018). Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. arXiv (Cornell University). 11 indexed citations

7.

Foerster, Jakob, Justin Gilmer, Jascha Sohl‐Dickstein, Jan Chorowski, & David Sussillo. (2017). Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. International Conference on Machine Learning. 1136–1145. 6 indexed citations

8.

Raghu, Maithra, Justin Gilmer, Jason Yosinski, & Jascha Sohl‐Dickstein. (2017). SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. arXiv (Cornell University). 30. 6076–6085. 76 indexed citations

9.

Raghu, Maithra, Justin Gilmer, Jason Yosinski, & Jascha Sohl‐Dickstein. (2017). SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. 4 indexed citations

10.

Kim, Been, Justin Gilmer, Fernanda Viégas, Úlfar Erlingsson, & Martin Wattenberg. (2017). TCAV: Relative concept importance testing with Linear Concept Activation Vectors. 12 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.

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