Quoc V. Le

Publications
Influence

Sequence to Sequence Learning with Neural Networks

Ilya Sutskever, Oriol Vinyals, Quoc V. Le
Computer Science
NIPS
10 September 2014

In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Expand

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1092
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Distributed Representations of Sentences and Documents

Quoc V. Le, Tomas Mikolov
Computer Science
ICML
16 May 2014

We propose Paragraph Vector, an unsupervised algorithm that learns fixed-length feature representations from variable-length pieces of texts, such as sentences, paragraphs, and documents. Expand

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858
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Mingxing Tan, Quoc V. Le
Computer Science, Mathematics
ICML
24 May 2019

We propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. Expand

1,961
457
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XLNet: Generalized Autoregressive Pretraining for Language Understanding

Z. Yang, Zihang Dai, Yiming Yang, J. Carbonell, R. Salakhutdinov, Quoc V. Le
Computer Science
NeurIPS
19 June 2019

We propose XLNet, a generalized autoregressive pretraining method that leverages the best of both AR language modeling and AE while avoiding their limitations. Expand

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Neural Architecture Search with Reinforcement Learning

Barret Zoph, Quoc V. Le
Computer Science, Mathematics
ICLR
4 November 2016

We use a recurrent network to generate the model descriptions of neural networks and train it with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. Expand

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346
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Tomas Mikolov, Quoc V. Le, Ilya Sutskever
Computer Science
ArXiv
16 September 2013

This paper develops a method that can automate the process of generating and extending dictionaries and translation tables for any language pairs. Expand

1,153
344
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Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Y. Wu, M. Schuster, +28 authors J. Dean
Computer Science
ArXiv
26 September 2016

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Expand

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Efficient Neural Architecture Search via Parameter Sharing

Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, J. Dean
Computer Science, Mathematics
ICML
9 February 2018

We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. Expand

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273
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Large Scale Distributed Deep Networks

J. Dean, G. Corrado, +9 authors A. Ng
Computer Science
NIPS
3 December 2012

We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas. Expand

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Regularized Evolution for Image Classifier Architecture Search

E. Real, A. Aggarwal, Y. Huang, Quoc V. Le
Computer Science
AAAI
5 February 2018

The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Expand

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212
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