Jonathan Frankle

3.4k total citations
12 papers, 425 citations indexed

About

Jonathan Frankle is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Jonathan Frankle has authored 12 papers receiving a total of 425 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 2 papers in Information Systems. Recurrent topics in Jonathan Frankle's work include Advanced Neural Network Applications (5 papers), Adversarial Robustness in Machine Learning (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Jonathan Frankle is often cited by papers focused on Advanced Neural Network Applications (5 papers), Adversarial Robustness in Machine Learning (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Jonathan Frankle collaborates with scholars based in United States and Israel. Jonathan Frankle's co-authors include Michael Carbin, Peter-Michael Osera, David Walker, Steve Zdancewic, Gintare Karolina Dziugaite, Daniel M. Roy, Ari S. Morcos, Yang Zhang, Zhangyang Wang and David J. Schwab and has published in prestigious journals such as ACM SIGPLAN Notices, USENIX Security Symposium and arXiv (Cornell University).

In The Last Decade

Jonathan Frankle

12 papers receiving 410 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jonathan Frankle United States	8	315	244	41	27	21	12	425
Yunsheng Bai United States	7	183 0.6×	130 0.5×	48 1.2×	40 1.5×	25 1.2×	20	329
Christoph Goller Germany	6	352 1.1×	82 0.3×	63 1.5×	15 0.6×	25 1.2×	12	466
Jinjing Zhou China	4	284 0.9×	161 0.7×	47 1.1×	48 1.8×	11 0.5×	5	391
Chuang Lin China	10	176 0.6×	360 1.5×	18 0.4×	28 1.0×	25 1.2×	27	484
Lingfan Yu China	4	188 0.6×	114 0.5×	40 1.0×	57 2.1×	13 0.6×	6	316
Xiaochen Lian China	8	111 0.4×	196 0.8×	39 1.0×	10 0.4×	49 2.3×	13	331
Jennifer Gillenwater United States	10	492 1.6×	115 0.5×	96 2.3×	10 0.4×	41 2.0×	19	622
Sampath Kannan United States	13	160 0.5×	169 0.7×	29 0.7×	18 0.7×	37 1.8×	37	406
Yu Yu China	10	253 0.8×	73 0.3×	94 2.3×	33 1.2×	44 2.1×	47	361

Countries citing papers authored by Jonathan Frankle

Since Specialization

Citations

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

Fields of papers citing papers by Jonathan Frankle

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonathan Frankle

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

All Works

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12 of 12 papers shown

Gokaslan, Aaron, A. Feder Cooper, Jasmine Collins, et al.. (2024). Common Canvas: Open Diffusion Models Trained on Creative-Commons Images. 8250–8260. 1 indexed citations

Saphra, Naomi, et al.. (2024). Dynamic Masking Rate Schedules for MLM Pretraining. 477–487. 1 indexed citations

Frankle, Jonathan, David J. Schwab, & Ari S. Morcos. (2021). Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs. arXiv (Cornell University). 2 indexed citations

Chen, Tianlong, Jonathan Frankle, Shiyu Chang, et al.. (2021). The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models. 16301–16311. 44 indexed citations

Frankle, Jonathan, David J. Schwab, & Ari S. Morcos. (2020). The Early Phase of Neural Network Training. arXiv (Cornell University). 15 indexed citations

Frankle, Jonathan & Michael Carbin. (2019). The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.. DSpace@MIT (Massachusetts Institute of Technology). 237 indexed citations

Frankle, Jonathan, Gintare Karolina Dziugaite, Daniel M. Roy, & Michael Carbin. (2019). Mode Connectivity and Sparse Neural Networks. 1 indexed citations

Frankle, Jonathan, Gintare Karolina Dziugaite, Daniel M. Roy, & Michael Carbin. (2019). The Lottery Ticket Hypothesis at Scale. 25 indexed citations

Frankle, Jonathan, et al.. (2018). Practical Accountability of Secret Processes. USENIX Security Symposium. 2018. 657–674. 10 indexed citations

10.

Frankle, Jonathan & Michael Carbin. (2018). The Lottery Ticket Hypothesis: Training Pruned Neural Networks.. 46 indexed citations

11.

Frankle, Jonathan, Peter-Michael Osera, David Walker, & Steve Zdancewic. (2016). Example-directed synthesis: a type-theoretic interpretation. ACM SIGPLAN Notices. 51(1). 802–815. 9 indexed citations

12.

Frankle, Jonathan, Peter-Michael Osera, David Walker, & Steve Zdancewic. (2016). Example-directed synthesis: a type-theoretic interpretation. 802–815. 34 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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