Citations per year, relative to Deirdre Quillen Deirdre Quillen (= 1×)
peers
Alex Irpan
Countries citing papers authored by Deirdre Quillen
Since
Specialization
Citations
This map shows the geographic impact of Deirdre Quillen'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 Deirdre Quillen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deirdre Quillen more than expected).
This network shows the impact of papers produced by Deirdre Quillen. 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 Deirdre Quillen. The network helps show where Deirdre Quillen may publish in the future.
Co-authorship network of co-authors of Deirdre Quillen
This figure shows the co-authorship network connecting the top 25 collaborators of Deirdre Quillen.
A scholar is included among the top collaborators of Deirdre Quillen 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 Deirdre Quillen. Deirdre Quillen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
3 of 3 papers shown
1.
Yu, Tianhe, Deirdre Quillen, Zhanpeng He, et al.. (2019). Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning. 2019. 1094–1100.27 indexed citations
Kalashnikov, Dmitry, Alex Irpan, Peter Pástor, et al.. (2018). QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. 651–673.71 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.