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 deep structured semantic models for web search using clickthrough data
20131.1k citationsPo-Sen Huang, Xiaodong He et al.profile →
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 Po-Sen Huang'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 Po-Sen Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Po-Sen Huang more than expected).
This network shows the impact of papers produced by Po-Sen Huang. 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 Po-Sen Huang. The network helps show where Po-Sen Huang may publish in the future.
Co-authorship network of co-authors of Po-Sen Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Po-Sen Huang.
A scholar is included among the top collaborators of Po-Sen Huang 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 Po-Sen Huang. Po-Sen Huang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Welbl, Johannes, Po-Sen Huang, Robert Stanforth, et al.. (2020). Towards Verified Robustness under Text Deletion Interventions. International Conference on Learning Representations.3 indexed citations
Wang, Chenglong, Po-Sen Huang, Alex Polozov, Marc Brockschmidt, & Rishabh Singh. (2018). Execution-Guided Neural Program Decoding. arXiv (Cornell University).15 indexed citations
8.
Shen, Yelong, Jianshu Chen, Po-Sen Huang, Yuqing Guo, & Jianfeng Gao. (2018). M-Walk: Learning to Walk in Graph with Monte Carlo Tree Search. arXiv (Cornell University).1 indexed citations
Huang, Po-Sen, Xiaodong He, Jianfeng Gao, et al.. (2013). Learning deep structured semantic models for web search using clickthrough data. 2333–2338.1116 indexed citations breakdown →
Huang, Po-Sen, Thyagaraju Damarla, & Mark Hasegawa‐Johnson. (2011). Multi-sensory features for personnel detection at border crossings. International Conference on Information Fusion. 1–8.8 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.