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Quoc V. Le
Researcher at Google
Publications - 229
Citations - 127721
Quoc V. Le is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Language model. The author has an hindex of 103, co-authored 217 publications receiving 101217 citations. Previous affiliations of Quoc V. Le include Northwestern University & Tel Aviv University.
Papers
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Proceedings ArticleDOI
AutoAugment: Learning Augmentation Strategies From Data
TL;DR: This paper describes a simple procedure called AutoAugment to automatically search for improved data augmentation policies, which achieves state-of-the-art accuracy on CIFAR-10, CIFar-100, SVHN, and ImageNet (without additional data).
Posted Content
Building high-level features using large scale unsupervised learning
Quoc V. Le,Marc'Aurelio Ranzato,Rajat Monga,Matthieu Devin,Kai Chen,Greg S. Corrado,Jeffrey Dean,Andrew Y. Ng +7 more
TL;DR: In this paper, a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization was used to train a face detector without having to label images as containing a face or not.
Posted Content
A Neural Conversational Model
Oriol Vinyals,Quoc V. Le +1 more
TL;DR: A simple approach to conversational modeling which uses the recently proposed sequence to sequence framework, and is able to extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles.
Journal ArticleDOI
Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski,Jennimaria Palomaki,Olivia Redfield,Michael Collins,Ankur P. Parikh,Chris Alberti,Danielle Epstein,Illia Polosukhin,Jacob Devlin,Kenton Lee,Kristina Toutanova,Llion Jones,Matthew Kelcey,Ming-Wei Chang,Andrew M. Dai,Jakob Uszkoreit,Quoc V. Le,Slav Petrov +17 more
TL;DR: The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems; demonstrating high human upper bounds on these metrics; and establishing baseline results using competitive methods drawn from related literature.
Posted Content
Exploiting Similarities among Languages for Machine Translation
TL;DR: This method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data and uses distributed representation of words and learns a linear mapping between vector spaces of languages.