Geoff Pleiss

5.4k total citations · 2 hit papers
10 papers, 968 citations indexed

About

Geoff Pleiss is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Geoff Pleiss has authored 10 papers receiving a total of 968 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Control and Systems Engineering and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Geoff Pleiss's work include Neural Networks and Applications (2 papers), Machine Learning and Data Classification (2 papers) and Gaussian Processes and Bayesian Inference (2 papers). Geoff Pleiss is often cited by papers focused on Neural Networks and Applications (2 papers), Machine Learning and Data Classification (2 papers) and Gaussian Processes and Bayesian Inference (2 papers). Geoff Pleiss collaborates with scholars based in United States, China and Canada. Geoff Pleiss's co-authors include Kilian Q. Weinberger, Zhuang Liu, Chuan Guo, Gao Huang, Yu Sun, Laurens van der Maaten, Manish Raghavan, Felix Wu, Jon Kleinberg and Andrew Gordon Wilson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Hydrometeorology.

In The Last Decade

Geoff Pleiss

10 papers receiving 916 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.

Convolutional Networks with Dense Connectivity

2019 379 citations Gao Huang, Zhuang Liu et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
On calibration of modern neural networks

2017 338 citations Chuan Guo, Geoff Pleiss et al. International Conference on Machine Learning profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Geoff Pleiss United States	7	535	325	106	60	51	10	968
Piotr Mardziel United States	8	520 1.0×	322 1.0×	135 1.3×	27 0.5×	44 0.9×	12	906
Jörn-Henrik Jacobsen Canada	4	530 1.0×	249 0.8×	101 1.0×	39 0.7×	28 0.5×	6	891
Eric Eaton United States	20	670 1.3×	279 0.9×	116 1.1×	17 0.3×	48 0.9×	64	1.2k
Zhifei Zhang China	18	472 0.9×	508 1.6×	33 0.3×	32 0.5×	52 1.0×	83	1.5k
Hugo L. Hammer Norway	13	348 0.7×	141 0.4×	164 1.5×	14 0.2×	39 0.8×	99	913
David López-Paz Germany	13	497 0.9×	312 1.0×	64 0.6×	29 0.5×	16 0.3×	26	867
Arka Pal United States	2	681 1.3×	578 1.8×	58 0.5×	14 0.2×	28 0.5×	2	1.2k
Christopher J. Anders Germany	7	373 0.7×	93 0.3×	52 0.5×	22 0.4×	34 0.7×	10	849
Levent Sagun United States	9	433 0.8×	382 1.2×	65 0.6×	5 0.1×	54 1.1×	18	867

Countries citing papers authored by Geoff Pleiss

Since Specialization

Citations

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

Fields of papers citing papers by Geoff Pleiss

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Geoff Pleiss

This figure shows the co-authorship network connecting the top 25 collaborators of Geoff Pleiss. A scholar is included among the top collaborators of Geoff Pleiss 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 Geoff Pleiss. Geoff Pleiss 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.

Mallayya, Krishnanand, Matthew Krogstad, Geoff Pleiss, et al.. (2022). Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction. Proceedings of the National Academy of Sciences. 119(24). e2109665119–e2109665119. 27 indexed citations

2.

Knighton, James, et al.. (2019). Potential Predictability of Regional Precipitation and Discharge Extremes Using Synoptic-Scale Climate Information via Machine Learning: An Evaluation for the Eastern Continental United States. Journal of Hydrometeorology. 20(5). 883–900. 16 indexed citations

3.

Pleiss, Geoff, et al.. (2019). Detecting Noisy Training Data with Loss Curves. 1 indexed citations

4.

Pleiss, Geoff, et al.. (2019). Neural Network Out-of-Distribution Detection for Regression Tasks. 1 indexed citations

5.

Huang, Gao, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, & Kilian Q. Weinberger. (2019). Convolutional Networks with Dense Connectivity. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(12). 8704–8716. 379 indexed citations breakdown →

6.

Gardner, Jacob R., Geoff Pleiss, Kilian Q. Weinberger, David Bindel, & Andrew Gordon Wilson. (2018). GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. Neural Information Processing Systems. 31. 7576–7586. 57 indexed citations

7.

Gardner, Jacob R., et al.. (2018). Product kernel interpolation for scalable gaussian processes. International Conference on Artificial Intelligence and Statistics. 1407–1416. 5 indexed citations

8.

Guo, Chuan, Geoff Pleiss, Yu Sun, & Kilian Q. Weinberger. (2017). On calibration of modern neural networks. International Conference on Machine Learning. 1321–1330. 338 indexed citations breakdown →

9.

Huang, Gao, Yixuan Li, Geoff Pleiss, et al.. (2017). Snapshot Ensembles: Train 1, get M for free. arXiv (Cornell University). 53 indexed citations

10.

Pleiss, Geoff, Manish Raghavan, Felix Wu, Jon Kleinberg, & Kilian Q. Weinberger. (2017). On Fairness and Calibration. arXiv (Cornell University). 30. 5680–5689. 91 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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