Jonas Geiping

1.9k total citations
16 papers, 148 citations indexed

About

Jonas Geiping is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Jonas Geiping has authored 16 papers receiving a total of 148 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 2 papers in Computational Mechanics. Recurrent topics in Jonas Geiping's work include Adversarial Robustness in Machine Learning (4 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Advanced Vision and Imaging (2 papers). Jonas Geiping is often cited by papers focused on Adversarial Robustness in Machine Learning (4 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Advanced Vision and Imaging (2 papers). Jonas Geiping collaborates with scholars based in United States and Germany. Jonas Geiping's co-authors include Tom Goldstein, Micah Goldblum, Gowthami Somepalli, Arpit Bansal, Hongmin Chu, Soumyadip Sengupta, Avi Schwarzschild, Gavin Taylor, Wei Huang and Liam Fowl and has published in prestigious journals such as arXiv (Cornell University), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) and International Conference on Learning Representations.

In The Last Decade

Jonas Geiping

13 papers receiving 143 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonas Geiping United States 4 85 54 17 14 11 16 148
Tongzheng Ren United States 4 72 0.8× 54 1.0× 18 1.1× 6 0.4× 4 0.4× 10 121
Parmida Atighehchian Canada 2 114 1.3× 43 0.8× 29 1.7× 16 1.1× 10 0.9× 2 182
Shant Navasardyan United States 6 201 2.4× 28 0.5× 40 2.4× 24 1.7× 10 0.9× 20 232
Hila Chefer Israel 4 169 2.0× 47 0.9× 48 2.8× 20 1.4× 12 1.1× 4 226
Wendong Zhang China 6 128 1.5× 26 0.5× 11 0.6× 8 0.6× 8 0.7× 16 147
Stan Weixian Lei Singapore 4 211 2.5× 68 1.3× 42 2.5× 22 1.6× 12 1.1× 5 253
Yuchao Gu Singapore 3 165 1.9× 26 0.5× 42 2.5× 19 1.4× 12 1.1× 7 202
Jay Zhangjie Wu Singapore 4 202 2.4× 43 0.8× 48 2.8× 21 1.5× 15 1.4× 8 243
Riccardo Corvi Italy 4 132 1.6× 47 0.9× 3 0.2× 9 0.6× 3 0.3× 4 180
Oran Gafni Israel 3 145 1.7× 26 0.5× 26 1.5× 29 2.1× 13 1.2× 4 167

Countries citing papers authored by Jonas Geiping

Since Specialization
Citations

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

Fields of papers citing papers by Jonas Geiping

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonas Geiping

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

All Works

16 of 16 papers shown
1.
Singh, Siddharth, et al.. (2024). Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers. 1–14. 3 indexed citations
2.
Geiping, Jonas, et al.. (2024). Object Recognition as Next Token Prediction. 16645–16656. 1 indexed citations
3.
4.
Wen, Yuxin, Jonas Geiping, Micah Goldblum, & Tom Goldstein. (2023). STYX: Adaptive Poisoning Attacks Against Byzantine-Robust Defenses in Federated Learning. 1–5. 2 indexed citations
5.
6.
Geiping, Jonas, et al.. (2023). Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. 41259–41282.
7.
Geiping, Jonas, et al.. (2023). Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images. 58047–58063.
8.
Somepalli, Gowthami, et al.. (2023). Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models. 6048–6058. 65 indexed citations
9.
Bansal, Arpit, Hongmin Chu, Avi Schwarzschild, et al.. (2023). Universal Guidance for Diffusion Models. 843–852. 62 indexed citations
10.
Fowl, Liam, et al.. (2022). Poisons that are learned faster are more effective. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 197–204. 2 indexed citations
11.
Fowl, Liam, et al.. (2021). Protecting Proprietary Data: Poisoning for Secure Dataset Release. 1 indexed citations
12.
Huang, Wei, Jonas Geiping, Liam Fowl, Gavin Taylor, & Tom Goldstein. (2020). MetaPoison: Practical General-purpose Clean-label Data Poisoning. arXiv (Cornell University). 33. 12080–12091. 3 indexed citations
13.
Goldblum, Micah, Jonas Geiping, Avi Schwarzschild, Michael Moeller, & Tom Goldstein. (2020). Truth or backpropaganda? An empirical investigation of deep learning theory. International Conference on Learning Representations. 2 indexed citations
14.
Geiping, Jonas, et al.. (2019). Piecewise Rigid Scene Flow with Implicit Motion Segmentation. 1758–1765. 3 indexed citations
15.
Geiping, Jonas, et al.. (2016). Multiframe Motion Coupling via Infimal Convolution Regularization for Video Super Resolution.. arXiv (Cornell University). 1 indexed citations
16.
Burger, Martin, et al.. (2014). Comparison of Topology-preserving Segmentation Methods and Application to Mitotic Cell Tracking. 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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