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.
INTERFEROME v2.0: an updated database of annotated interferon-regulated genes
2012610 citationsSamuel C. Forster, Simon Yu et al.Nucleic Acids Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Anitha Kannan'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 Anitha Kannan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anitha Kannan more than expected).
This network shows the impact of papers produced by Anitha Kannan. 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 Anitha Kannan. The network helps show where Anitha Kannan may publish in the future.
Co-authorship network of co-authors of Anitha Kannan
This figure shows the co-authorship network connecting the top 25 collaborators of Anitha Kannan.
A scholar is included among the top collaborators of Anitha Kannan 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 Anitha Kannan. Anitha Kannan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kalyan, Ashwin, Stefan Lee, Anitha Kannan, & Dhruv Batra. (2018). Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations. International Conference on Machine Learning. 2449–2458.1 indexed citations
4.
Kannan, Anitha, et al.. (2018). Learning from the experts: From expert systems to machine learned diagnosis models. 227–243.2 indexed citations
5.
Lu, Jiasen, Anitha Kannan, Jianwei Yang, Devi Parikh, & Dhruv Batra. (2017). Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model. Neural Information Processing Systems. 30. 314–324.37 indexed citations
Frey, Brendan J. & Anitha Kannan. (2000). Accumulator Networks: Suitors of Local Probability Propagation. Neural Information Processing Systems. 13. 486–492.5 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.