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.
Seasonal Arctic sea ice forecasting with probabilistic deep learning
2021158 citationsTom R. Andersson, J. Scott Hosking et al.Nature Communicationsprofile →
Citations per year, relative to Brooks Paige Brooks Paige (= 1×)
peers
Yuyi Wang
Countries citing papers authored by Brooks Paige
Since
Specialization
Citations
This map shows the geographic impact of Brooks Paige'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 Brooks Paige with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brooks Paige more than expected).
This network shows the impact of papers produced by Brooks Paige. 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 Brooks Paige. The network helps show where Brooks Paige may publish in the future.
Co-authorship network of co-authors of Brooks Paige
This figure shows the co-authorship network connecting the top 25 collaborators of Brooks Paige.
A scholar is included among the top collaborators of Brooks Paige 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 Brooks Paige. Brooks Paige is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Andersson, Tom R., J. Scott Hosking, María Pérez‐Ortiz, et al.. (2021). Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications. 12(1). 5124–5124.158 indexed citations breakdown →
6.
Paige, Brooks, et al.. (2021). Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. International Conference on Learning Representations.1 indexed citations
Law, Stephen, Brooks Paige, & Chris Russell. (2019). Take a Look Around. ACM Transactions on Intelligent Systems and Technology. 10(5). 1–19.114 indexed citations
11.
Bradshaw, John, Matt J. Kusner, Brooks Paige, Marwin Segler, & José Miguel Hernández-Lobato. (2019). Generating molecules via chemical reactions. UCL Discovery (University College London).1 indexed citations
12.
Law, Stephen, Brooks Paige, & Chris Russell. (2019). Take a Look Around. arXiv (Cornell University).27 indexed citations
13.
Bradshaw, John, Brooks Paige, Matt J. Kusner, Marwin Segler, & José Miguel Hernández-Lobato. (2019). A Model to Search for Synthesizable Molecules. PolyPublie (École Polytechnique de Montréal). 32. 7905–7917.26 indexed citations
14.
Wu, Hao, Alican Bozkurt, N. Siddharth, et al.. (2019). Structured Disentangled Representations. UCL Discovery (University College London). 2525–2534.23 indexed citations
15.
Wu, Hao, et al.. (2018). Hierarchical Disentangled Representations.2 indexed citations
Siddharth, N., Brooks Paige, Alban Desmaison, et al.. (2017). Learning Disentangled Representations in Deep Generative Models.5 indexed citations
18.
Paige, Brooks, Dino Sejdinović, & Frank Wood. (2016). Super-sampling with a reservoir. Cambridge University Engineering Department Publications Database. 567–576.1 indexed citations
Paige, Brooks, et al.. (2013). Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. Cambridge University Engineering Department Publications Database. 26. 1304–1312.19 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.