Will Grathwohl

1.4k total citations
7 papers, 90 citations indexed

About

Will Grathwohl is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Will Grathwohl has authored 7 papers receiving a total of 90 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Signal Processing. Recurrent topics in Will Grathwohl's work include Generative Adversarial Networks and Image Synthesis (4 papers), Neural Networks and Applications (2 papers) and Adversarial Robustness in Machine Learning (2 papers). Will Grathwohl is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (4 papers), Neural Networks and Applications (2 papers) and Adversarial Robustness in Machine Learning (2 papers). Will Grathwohl collaborates with scholars based in Canada, United States and Germany. Will Grathwohl's co-authors include Joern-Henrik Jacobsen, David Duvenaud, Ricky T. Q. Chen, Jens Behrmann, Daniel Faissol, Jiachen Yang, Chase Cockrell, Gary An, Brenden K. Petersen and Richard S. Zemel and has published in prestigious journals such as The Journal of the Acoustical Society of America, Journal of Computational Biology and arXiv (Cornell University).

In The Last Decade

Will Grathwohl

7 papers receiving 85 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Will Grathwohl Canada	4	48	31	14	13	8	7	90
Konstantina Palla United Kingdom	5	39 0.8×	9 0.3×	7 0.5×	9 0.7×	1 0.1×	12	79
Max Vladymyrov United States	5	48 1.0×	59 1.9×	13 0.9×	9 0.7×	1 0.1×	9	91
Bingzheng Wei China	5	42 0.9×	34 1.1×	5 0.4×	3 0.2×		9	70
Samuel Chapman United States	5	54 1.1×	18 0.6×	18 1.3×		13 1.6×	7	113
Tri Dao United States	6	37 0.8×	20 0.6×	18 1.3×	2 0.2×	1 0.1×	12	74
Ousmane Dia United States	3	79 1.6×	35 1.1×	7 0.5×	3 0.2×	2 0.3×	5	95
Yuxuan Sun China	6	74 1.5×	37 1.2×	8 0.6×		2 0.3×	17	120
Mark Heimann United States	5	40 0.8×	11 0.4×	11 0.8×	28 2.2×		10	58
Joern-Henrik Jacobsen Canada	6	87 1.8×	52 1.7×	3 0.2×	20 1.5×		7	110

Countries citing papers authored by Will Grathwohl

Since Specialization

Citations

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

Fields of papers citing papers by Will Grathwohl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Will Grathwohl

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

All Works

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7 of 7 papers shown

1.

Grathwohl, Will, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, & Richard S. Zemel. (2020). Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. International Conference on Machine Learning. 1. 3732–3747. 3 indexed citations

2.

Grathwohl, Will, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, & Richard S. Zemel. (2020). Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. arXiv (Cornell University). 1 indexed citations

3.

Behrmann, Jens, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, & Joern-Henrik Jacobsen. (2019). Invertible Residual Networks. International Conference on Machine Learning. 573–582. 40 indexed citations

4.

Petersen, Brenden K., Jiachen Yang, Will Grathwohl, et al.. (2019). Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine. Journal of Computational Biology. 26(6). 597–604. 34 indexed citations

5.

Fetaya, Ethan, Joern-Henrik Jacobsen, Will Grathwohl, & Richard S. Zemel. (2019). Understanding the Limitations of Conditional Generative Models. arXiv (Cornell University). 5 indexed citations

6.

Grathwohl, Will, Elliot Creager, Seyed Kamyar Seyed Ghasemipour, & Richard S. Zemel. (2018). Gradient-based Optimization of Neural Network Architecture.. International Conference on Learning Representations. 5 indexed citations

7.

Whalen, D. H., Khalil Iskarous, Will Grathwohl, & Michael Proctor. (2010). Using digital ultrasound to investigate trill vibration.. The Journal of the Acoustical Society of America. 128(4_Supplement). 2289–2289. 2 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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