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.
Effective Approaches to Attention-based Neural Machine Translation
20154.9k citationsThang Luong, Hieu Pham et al.profile →
This map shows the geographic impact of Thang Luong'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 Thang Luong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thang Luong more than expected).
This network shows the impact of papers produced by Thang Luong. 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 Thang Luong. The network helps show where Thang Luong may publish in the future.
Co-authorship network of co-authors of Thang Luong
This figure shows the co-authorship network connecting the top 25 collaborators of Thang Luong.
A scholar is included among the top collaborators of Thang Luong 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 Thang Luong. Thang Luong is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kudugunta, Sneha, Yanping Huang, Ankur Bapna, et al.. (2021). Exploring Routing Strategies for Multilingual Mixture-of-Experts Models.1 indexed citations
5.
Ngiam, Jiquan, Yanping Huang, Thang Luong, et al.. (2020). Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout. Neural Information Processing Systems. 33. 2039–2050.1 indexed citations
6.
Xie, Qizhe, Zihang Dai, Eduard Hovy, Thang Luong, & Quoc V. Le. (2020). Unsupervised Data Augmentation for Consistency Training. Neural Information Processing Systems. 33. 6256–6268.77 indexed citations
7.
Yang, Zhilin, Thang Luong, Russ R. Salakhutdinov, & Quoc V. Le. (2019). Mixtape: Breaking the Softmax Bottleneck Efficiently. Neural Information Processing Systems. 32. 15922–15930.4 indexed citations
8.
Clark, Kevin B., Thang Luong, & Quoc V. Le. (2018). Cross-View Training for Semi-Supervised Learning. International Conference on Learning Representations.2 indexed citations
9.
Luong, Thang, Kyunghyun Cho, & Christopher D. Manning. (2016). Neural Machine Translation. Meeting of the Association for Computational Linguistics.11 indexed citations
10.
Li, Jiwei, Thang Luong, & Dan Jurafsky. (2015). A Hierarchical Neural Autoencoder for Paragraphs and Documents. 1106–1115.293 indexed citations breakdown →
11.
Luong, Thang, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, & Wojciech Zaremba. (2015). Addressing the Rare Word Problem in Neural Machine Translation. 11–19.390 indexed citations breakdown →
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.