Colorado Reed

970 total citations · 1 hit paper
11 papers, 453 citations indexed

About

Colorado Reed is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Global and Planetary Change. According to data from OpenAlex, Colorado Reed has authored 11 papers receiving a total of 453 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Global and Planetary Change. Recurrent topics in Colorado Reed's work include Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Neural Network Applications (2 papers). Colorado Reed is often cited by papers focused on Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Neural Network Applications (2 papers). Colorado Reed collaborates with scholars based in United States, Australia and Argentina. Colorado Reed's co-authors include Trevor Darrell, Kurt Keutzer, Björn Hartmann, Maneesh Agrawala, Amy Pavel, Adam K. Kochanski, Craig B. Clements, Patrick T. Brown, Ankur Mahesh and Steven J. Davis and has published in prestigious journals such as Nature, Statistical Analysis and Data Mining The ASA Data Science Journal and Proceedings of the International Astronomical Union.

In The Last Decade

Colorado Reed

11 papers receiving 445 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.

Climate warming increases extreme daily wildfire growth risk in California

2023 97 citations Patrick T. Brown, Ankur Mahesh et al. Nature profile →

Peers

Countries citing papers authored by Colorado Reed

Since Specialization

Citations

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

Fields of papers citing papers by Colorado Reed

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Colorado Reed

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

All Works

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11 of 11 papers shown

#	Work	Indexed citations
1	Climate warming increases extreme daily wildfire growth risk in California breakdown → 2023 ·Nature ·Patrick T. Brown, (unknown), Ankur Mahesh, Colorado Reed, (unknown), Steven J. Davis, Adam K. Kochanski, Craig B. Clements	97
2	Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning 2023 ·Colorado Reed, Ritwik Gupta, (unknown), (unknown), Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, Trevor Darrell	90
3	Invariant Information Bottleneck for Domain Generalization 2022 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Bo Li, Yifei Shen, (unknown), (unknown), Colorado Reed, Dongsheng Li, Kurt Keutzer, Han Zhao	55
4	Self-Supervised Pretraining Improves Self-Supervised Pretraining 2022 ·2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) ·Colorado Reed, Xiangyu Yue, (unknown), Sayna Ebrahimi, (unknown), (unknown), Bo Li, Shanghang Zhang, Devin Guillory, Sean L. Metzger, Kurt Keutzer, Trevor Darrell	45
5	DETReg: Unsupervised Pretraining with Region Priors for Object Detection 2022 ·2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ·Amir Bar, Xin Wang, (unknown), Colorado Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson	80
6	Video digests 2014 ·Amy Pavel, Colorado Reed, Björn Hartmann, Maneesh Agrawala	64
7	Latent Dirichlet Allocation: Towards a Deeper Understanding 2012 ·Colorado Reed	9
8	Semi‐supervised Eigenbasis novelty detection 2012 ·Statistical Analysis and Data Mining The ASA Data Science Journal ·David R. Thompson, Walid A. Majid, Colorado Reed, Kiri L. Wagstaff	2
9	Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients 2011 ·David R. Thompson, Walid A. Majid, Colorado Reed, Kiri L. Wagstaff	1
10	Classification of ASKAP VAST Radio Light Curves 2011 ·Proceedings of the International Astronomical Union ·Umaa Rebbapragada, Kitty Lo, Kiri L. Wagstaff, Colorado Reed, Tara Murphy, David R. Thompson	4
11	What's trending? 2011 ·Colorado Reed, (unknown), Padmini Srinivasan	6

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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