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.
Hierarchical Reinforcement Learning
2021217 citationsShubham Pateria, Budhitama Subagdja 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 Ah‐Hwee Tan'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 Ah‐Hwee Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ah‐Hwee Tan more than expected).
This network shows the impact of papers produced by Ah‐Hwee Tan. 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 Ah‐Hwee Tan. The network helps show where Ah‐Hwee Tan may publish in the future.
Co-authorship network of co-authors of Ah‐Hwee Tan
This figure shows the co-authorship network connecting the top 25 collaborators of Ah‐Hwee Tan.
A scholar is included among the top collaborators of Ah‐Hwee Tan 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 Ah‐Hwee Tan. Ah‐Hwee Tan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tan, Ah‐Hwee, et al.. (2015). An adaptive computational model for personalized persuasion. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 61–67.14 indexed citations
Ślȩzak, Dominik, Ah‐Hwee Tan, James F. Peters, & Lars Schwabe. (2014). Brain Informatics and Health: International Conference, BIH 2014, Warsaw, Poland, August 11-14, 2014.Proceedings. Springer eBooks.1 indexed citations
Tan, Ah‐Hwee. (2007). Direct Code Access in Self-Organizing Neural Networks for Reinforcement Learning.. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1071–1076.26 indexed citations
17.
Jiang, Xing & Ah‐Hwee Tan. (2006). OntoSearch: a full-text search engine for the semantic web. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1325–1330.27 indexed citations
18.
Li, Jinyan, Qiang Yang, & Ah‐Hwee Tan. (2006). Proceedings of the 2006 international conference on Data Mining for Biomedical Applications.1 indexed citations
19.
Tan, Ah‐Hwee & Alex Galis. (2000). Active IP Network Node Developments. UCL Discovery (University College London).
20.
Tan, Ah‐Hwee. (1994). Adaptive Resonance Associative Map : A Neural Model for Heteroassociative Learning and Recall. International Conference on Neural Information Processing. 3(2). 731–736.1 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.