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.
Learning monocular reactive UAV control in cluttered natural environments
2013254 citationsStéphane Ross, Kumar Shaurya Shankar et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Debadeepta Dey
Since
Specialization
Citations
This map shows the geographic impact of Debadeepta Dey'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 Debadeepta Dey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debadeepta Dey more than expected).
This network shows the impact of papers produced by Debadeepta Dey. 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 Debadeepta Dey. The network helps show where Debadeepta Dey may publish in the future.
Co-authorship network of co-authors of Debadeepta Dey
This figure shows the co-authorship network connecting the top 25 collaborators of Debadeepta Dey.
A scholar is included among the top collaborators of Debadeepta Dey 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 Debadeepta Dey. Debadeepta Dey is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Phanishayee, Amar, et al.. (2021). Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size. International Conference on Machine Learning. 5731–5741.6 indexed citations
Dey, Debadeepta, et al.. (2018). Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy. arXiv (Cornell University).2 indexed citations
Dey, Debadeepta, Tommy Liu, Martial Hebert, & J. Andrew Bagnell. (2012). Contextual Sequence Prediction via Submodular Function Optimization.2 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.