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.
EfficientDet: Scalable and Efficient Object Detection
20205.3k citationsMingxing Tan, Ruoming Pang et al.profile →
Distributed Representations of Sentences and Documents
20143.3k citationsQuoc V. Le et al.arXiv (Cornell University)profile →
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
20192.1k citationsBarret Zoph, Quoc V. Le et al.arXiv (Cornell University)profile →
This map shows the geographic impact of Quoc V. Le'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 Quoc V. Le with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quoc V. Le more than expected).
This network shows the impact of papers produced by Quoc V. Le. 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 Quoc V. Le. The network helps show where Quoc V. Le may publish in the future.
Co-authorship network of co-authors of Quoc V. Le
This figure shows the co-authorship network connecting the top 25 collaborators of Quoc V. Le.
A scholar is included among the top collaborators of Quoc V. Le 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 Quoc V. Le. Quoc V. Le is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Süzgün, Mirac, Nathan Scales, Nathanael Schärli, et al.. (2023). Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them. 13003–13051.121 indexed citations breakdown →
3.
Lee, Jason, Najoung Kim, Yi Tay, & Quoc V. Le. (2023). Inverse Scaling Can Become U-Shaped. OpenBU (Boston University). 15580–15591.15 indexed citations
4.
Tan, Mingxing, Ruoming Pang, & Quoc V. Le. (2020). EfficientDet: Scalable and Efficient Object Detection. 10778–10787.5281 indexed citations breakdown →
5.
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
6.
Dai, Zihang, Guokun Lai, Yiming Yang, & Quoc V. Le. (2020). Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. arXiv (Cornell University). 33. 4271–4282.17 indexed citations
7.
Yang, Brandon, Gabriel Bender, Quoc V. Le, & Jiquan Ngiam. (2019). Soft Conditional Computation.. arXiv (Cornell University).2 indexed citations
8.
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
Smith, Samuel, Pieter-Jan Kindermans, Chris Ying, & Quoc V. Le. (2018). Don't decay the learning rate, increase the batch size. arXiv (Cornell University).106 indexed citations
11.
Ramachandran, Prajit & Quoc V. Le. (2018). Diversity and Depth in Per-Example Routing Models. International Conference on Learning Representations.9 indexed citations
12.
Chen, Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, & Ni Lao. (2018). Memory Augmented Policy Optimization for Program Synthesis with Generalization. arXiv (Cornell University).4 indexed citations
13.
Clark, Kevin B., Thang Luong, & Quoc V. Le. (2018). Cross-View Training for Semi-Supervised Learning. International Conference on Learning Representations.2 indexed citations
Ngiam, Jiquan, et al.. (2010). Tiled convolutional neural networks. Neural Information Processing Systems. 23. 1279–1287.169 indexed citations
19.
McCann, Robert, et al.. (2005). Mapping maintenance for data integration systems. Very Large Data Bases. 1018–1029.37 indexed citations
20.
Gärtner, Thomas, Quoc V. Le, Simon Burton, Alex Smola, & S. V. N. Vishwanathan. (2005). Large-Scale Multiclass Transduction. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 18. 411–418.10 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.