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Computer Science

D-Index
110
Citations
143721
World Ranking
220
National Ranking
122

Overview

Quoc V. Le is affiliated with Google in the United States. Their main field of study is Computer Science, with a strong focus on Artificial Intelligence. The scientist's research extensively covers several subfields including Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Information Systems, and Signal Processing.

Their research work spans a range of topics such as Topic Modeling, Natural Language Processing Techniques, Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, Machine Learning and Data Classification, and Speech Recognition and Synthesis.

Quoc V. Le has published numerous papers in prominent venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Nature
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • International Journal of Mechatronics and Applied Mechanics
  • Dagstuhl Research Online Publication Server

Notable recent papers authored or co-authored by Quoc V. Le are:

  • "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" (2022, arXiv)
  • "Xlnet: Generalized Autoregressive Pretraining for Language Understanding" (2025, arXiv)
  • "Question Answering For Toxicological Information Extraction" (2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl))
  • "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision" (2021, arXiv)
  • "Scaling Instruction-Finetuned Language Models" (2022, arXiv)

Quoc V. Le frequently collaborates with other researchers in their field. Some of these frequent coauthors are:

  • Denny Zhou
  • Mingxing Tan
  • Hanxiao Liu
  • Ed H.
  • Adams Wei Yu

Best Publications

  • Sequence to Sequence Learning with Neural Networks

    Ilya Sutskever;Oriol Vinyals;Quoc V. Le

  • Distributed Representations of Sentences and Documents

    Quoc Le;Tomas Mikolov

  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le

  • XLNet: Generalized Autoregressive Pretraining for Language Understanding

    Zhilin Yang;Zihang Dai;Yiming Yang;Jaime G. Carbonell

  • Learning Transferable Architectures for Scalable Image Recognition

    Barret Zoph;Vijay Vasudevan;Jonathon Shlens;Quoc V. Le

  • Neural Architecture Search with Reinforcement Learning

    Barret Zoph;Quoc V. Le

  • Large Scale Distributed Deep Networks

    Jeffrey Dean;Greg Corrado;Rajat Monga;Kai Chen

  • Transformer-XL: Attentive Language Models beyond a Fixed-Length Context.

    Zihang Dai;Zhilin Yang;Yiming Yang;Jaime G. Carbonell

  • SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

    Daniel S. Park;William Chan;Yu Zhang;Chung-Cheng Chiu

  • Building high-level features using large scale unsupervised learning

    Quoc V. Le

  • Regularized Evolution for Image Classifier Architecture Search

    Esteban Real;Alok Aggarwal;Yanping Huang;Quoc V. Le

  • Building high-level features using large scale unsupervised learning

    Marc'aurelio Ranzato;Rajat Monga;Matthieu Devin;Kai Chen

  • AutoAugment: Learning Augmentation Strategies From Data

    Ekin D. Cubuk;Barret Zoph;Dandelion Mane;Vijay Vasudevan

  • Listen, attend and spell: A neural network for large vocabulary conversational speech recognition

    William Chan;Navdeep Jaitly;Quoc Le;Oriol Vinyals

  • Self-Training With Noisy Student Improves ImageNet Classification

    Qizhe Xie;Minh-Thang Luong;Eduard Hovy;Quoc V. Le

  • Scalable and accurate deep learning with electronic health records

    Alvin Rajkomar;Alvin Rajkomar;Eyal Oren;Kai Chen;Andrew M. Dai

  • Scalable and accurate deep learning for electronic health records

    Alvin Rajkomar;Eyal Oren;Kai Chen;Andrew M. Dai

  • A Neural Conversational Model

    Oriol Vinyals;Quoc V. Le

  • Exploiting Similarities among Languages for Machine Translation

    Tomas Mikolov;Quoc V. Le;Ilya Sutskever

  • Efficient Neural Architecture Search via Parameters Sharing

    Hieu Pham;Melody Y. Guan;Barret Zoph;Quoc V. Le

  • Natural Questions: A Benchmark for Question Answering Research

    Tom Kwiatkowski;Jennimaria Palomaki;Olivia Redfield;Michael Collins

  • Searching for Activation Functions

    Prajit Ramachandran;Barret Zoph;Quoc V. Le

  • Efficient Neural Architecture Search via Parameter Sharing

    Hieu Pham;Melody Y. Guan;Barret Zoph;Quoc V. Le

Frequent Co-Authors

Barret Zoph
Barret Zoph Google (United States)
Jeffrey Dean
Jeffrey Dean Google (United States)
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Andrew Y. Ng
Andrew Y. Ng Stanford University
Vijay K. Vasudevan
Vijay K. Vasudevan Google (United States)
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Samy Bengio
Samy Bengio Apple (United States)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Greg Corrado
Greg Corrado Google (United States)

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