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.
TVM: an automated end-to-end optimizing compiler for deep learning
2018522 citationsTianqi Chen, Thierry Moreau et al.Operating Systems Design and Implementationprofile →
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 Tianqi Chen'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 Tianqi Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tianqi Chen more than expected).
This network shows the impact of papers produced by Tianqi Chen. 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 Tianqi Chen. The network helps show where Tianqi Chen may publish in the future.
Co-authorship network of co-authors of Tianqi Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Tianqi Chen.
A scholar is included among the top collaborators of Tianqi Chen 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 Tianqi Chen. Tianqi Chen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zheng, Lianmin, Ruochen Liu, Junru Shao, et al.. (2021). TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers. Neural Information Processing Systems.8 indexed citations
11.
Roesch, Jared, et al.. (2019). Relay: A High-Level IR for Deep Learning.. arXiv (Cornell University).4 indexed citations
12.
Chen, Tianqi, Lianmin Zheng, Eddie Yan, et al.. (2018). Learning to Optimize Tensor Programs. arXiv (Cornell University). 31. 3389–3400.29 indexed citations
13.
Chen, Tianqi, Thierry Moreau, Ziheng Jiang, et al.. (2018). TVM: an automated end-to-end optimizing compiler for deep learning. Operating Systems Design and Implementation. 578–594.522 indexed citations breakdown →
14.
Ma, Yi-An, Tianqi Chen, & Emily B. Fox. (2015). A complete recipe for stochastic gradient MCMC. Neural Information Processing Systems. 28. 2917–2925.28 indexed citations
15.
Chen, Tianqi, Sameer Singh, Ben Taskar, & Carlos Guestrin. (2015). {Efficient Second-Order Gradient Boosting for Conditional Random Fields}. International Conference on Artificial Intelligence and Statistics. 147–155.12 indexed citations
16.
Chen, Tianqi, Hang Li, Qiang Yang, & Yong Yu. (2013). General Functional Matrix Factorization Using Gradient Boosting. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 28. 436–444.28 indexed citations
17.
QasemiZadeh, Behrang, Paul Buitelaar, Tianqi Chen, & Georgeta Bordea. (2012). Semi-Supervised Technical Term Tagging With Minimal User Feedback. Language Resources and Evaluation. 617–621.1 indexed citations
Zhao, Zheng, et al.. (2011). Rating Prediction with Informative Ensemble of Multi-Resolution Dynamic Models.. Knowledge Discovery and Data Mining. 75–97.5 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.