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.
Multiclass cancer diagnosis using tumor gene expression signatures
20011.5k citationsSridhar Ramaswamy, Pablo Tamayo et al.Proceedings of the National Academy of Sciencesprofile →
This map shows the geographic impact of Ryan Rifkin'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 Ryan Rifkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryan Rifkin more than expected).
This network shows the impact of papers produced by Ryan Rifkin. 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 Ryan Rifkin. The network helps show where Ryan Rifkin may publish in the future.
Co-authorship network of co-authors of Ryan Rifkin
This figure shows the co-authorship network connecting the top 25 collaborators of Ryan Rifkin.
A scholar is included among the top collaborators of Ryan Rifkin 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 Ryan Rifkin. Ryan Rifkin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mansinghka, Vikash K., Daniel M. Roy, Ryan Rifkin, & Joshua B. Tenenbaum. (2007). AClass: A simple, online, parallelizable algorithm for probabilistic classification. International Conference on Artificial Intelligence and Statistics. 315–322.4 indexed citations
3.
Rifkin, Ryan & Ross A. Lippert. (2007). Notes on Regularized Least Squares. DSpace@MIT (Massachusetts Institute of Technology).66 indexed citations
4.
Rifkin, Ryan, Jake Bouvrie, Sharat Chikkerur, et al.. (2007). Phonetic Classification Using Hierarchical, Feed-forward, Spectro-temporal Patch-based Architectures. DSpace@MIT (Massachusetts Institute of Technology).8 indexed citations
Mukherjee, Sayan & Ryan Rifkin. (2001). Support Vector Machine Classification of Microarray Data.122 indexed citations
16.
Rifkin, Ryan, et al.. (2001). The Audiomomma Music Recommendation System. DSpace@MIT (Massachusetts Institute of Technology).1 indexed citations
17.
Ramaswamy, Sridhar, Pablo Tamayo, Ryan Rifkin, et al.. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences. 98(26). 15149–15154.1458 indexed citations breakdown →
18.
Ball, Michael O., et al.. (1999). The Static Stochastic Ground Holding Problem with Aggregate Demands. Digital Repository at the University of Maryland (University of Maryland College Park).10 indexed citations
19.
Pontil, Massimiliano, Ryan Rifkin, & Theodoros Evgeniou. (1999). From Regression to Classication in Support Vector Machines.2 indexed citations
20.
Pontil, Massimiliano, Ryan Rifkin, & Theodoros Evgeniou. (1998). From Regression to Classification in Support Vector Machines. DSpace@MIT (Massachusetts Institute of Technology). 225–230.16 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.