Wee Sun Lee

990 total citations
14 papers, 582 citations indexed

About

Wee Sun Lee is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Wee Sun Lee has authored 14 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Computer Networks and Communications. Recurrent topics in Wee Sun Lee's work include Topic Modeling (4 papers), Reinforcement Learning in Robotics (3 papers) and Natural Language Processing Techniques (3 papers). Wee Sun Lee is often cited by papers focused on Topic Modeling (4 papers), Reinforcement Learning in Robotics (3 papers) and Natural Language Processing Techniques (3 papers). Wee Sun Lee collaborates with scholars based in Singapore, United States and United Kingdom. Wee Sun Lee's co-authors include Bing Liu, Xiaoli Li, Philip S. Yu, David Hsu, Yee Whye Teh, Jun Cai, Hwee Tou Ng, Min Chen, Emilio Frazzoli and D. Frank Hsu and has published in prestigious journals such as ACM Transactions on Multimedia Computing Communications and Applications, National University of Singapore and arXiv (Cornell University).

In The Last Decade

Wee Sun Lee

13 papers receiving 552 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Wee Sun Lee Singapore	9	412	156	89	84	48	14	582
Zhongyang Xiong China	15	423 1.0×	147 0.9×	35 0.4×	94 1.1×	105 2.2×	55	584
Kai Zhou United States	12	204 0.5×	88 0.6×	46 0.5×	132 1.6×	97 2.0×	53	412
Yongsoo Song South Korea	11	598 1.5×	103 0.7×	61 0.7×	146 1.7×	100 2.1×	15	731
Ahmed T. Sadiq Iraq	10	209 0.5×	103 0.7×	25 0.3×	60 0.7×	89 1.9×	82	368
Guoping Wang United States	10	117 0.3×	120 0.8×	31 0.3×	84 1.0×	79 1.6×	40	373
Huowang Chen China	9	164 0.4×	67 0.4×	18 0.2×	94 1.1×	99 2.1×	66	369
Yifang Sun Australia	10	204 0.5×	299 1.9×	20 0.2×	37 0.4×	54 1.1×	17	515
Amir Massoud Bidgoli Iran	9	99 0.2×	187 1.2×	29 0.3×	92 1.1×	168 3.5×	25	472
Lucija Brezočnik Slovenia	4	229 0.6×	55 0.4×	28 0.3×	36 0.4×	40 0.8×	9	358
Runfa Liao China	13	248 0.6×	98 0.6×	55 0.6×	111 1.3×	292 6.1×	26	764

Countries citing papers authored by Wee Sun Lee

Since Specialization

Citations

This map shows the geographic impact of Wee Sun Lee'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 Wee Sun Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wee Sun Lee more than expected).

Fields of papers citing papers by Wee Sun Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wee Sun Lee. 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 Wee Sun Lee. The network helps show where Wee Sun Lee may publish in the future.

Co-authorship network of co-authors of Wee Sun Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Wee Sun Lee. A scholar is included among the top collaborators of Wee Sun Lee 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 Wee Sun Lee. Wee Sun Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

14 of 14 papers shown

1.

Cao, Zhiguang, et al.. (2024). Hierarchical Neural Constructive Solver for Real-world TSP Scenarios. arXiv (Cornell University). 884–895. 2 indexed citations

2.

Kan, Min‐Yen, et al.. (2024). KeYric: Unsupervised Keywords Extraction and Expansion from Music for Coherent Lyrics Generation. ACM Transactions on Multimedia Computing Communications and Applications. 21(1). 1–28.

3.

Wang, Ye, et al.. (2021). AI-Lyricist. National University of Singapore. 1002–1011. 8 indexed citations

4.

Hsu, David, et al.. (2019). Learning To Grasp Under Uncertainty Using POMDPs. 2751–2757. 10 indexed citations

5.

Hsu, D. Frank, et al.. (2019). DESPOT-Alpha: Online POMDP Planning with Large State and Observation Spaces. 23 indexed citations

6.

Lee, Wee Sun, et al.. (2018). Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties. 47 indexed citations

7.

Chen, Min, Emilio Frazzoli, David Hsu, & Wee Sun Lee. (2016). POMDP-lite for robust robot planning under uncertainty. 5427–5433. 31 indexed citations

8.

Hsu, David, et al.. (2016). Act to See and See to Act: POMDP planning for objects search in clutter. 43 indexed citations

9.

Lee, Wee Sun, et al.. (2015). POMDP to the Rescue: Boosting Performance for Robocup Rescue. 65. 5294–5299. 3 indexed citations

10.

Lee, Wee Sun, et al.. (2007). Improving Word Sense Disambiguation Using Topic Features. National University of Singapore. 1015–1023. 25 indexed citations

11.

Hsu, David, et al.. (2007). Accelerating point-based POMDP algorithms through successive approximations of the optimal reachable space. 3 indexed citations

12.

Cai, Jun, Wee Sun Lee, & Yee Whye Teh. (2007). NUS-ML. 249–252. 30 indexed citations

13.

Ng, Hwee Tou, et al.. (2005). Word sense disambiguation with semi-supervised learning. National University of Singapore. 1093–1098. 25 indexed citations

14.

Liu, Bing, Wee Sun Lee, Philip S. Yu, & Xiaoli Li. (2002). Partially Supervised Classification of Text Documents. International Conference on Machine Learning. 387–394. 332 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.

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