Quoc V. Le

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Research Scientist at Google.

Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of c...
05/06/2019 ∙ by Andrew Howard, et al. ∙ 53 ∙ share

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High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

Predicting future video frames is extremely challenging, as there are ma...
11/05/2019 ∙ by Ruben Villegas, et al. ∙ 37 ∙ share

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Self-training with Noisy Student improves ImageNet classification

We present a simple self-training method that achieves 87.4 on ImageNet,...
11/11/2019 ∙ by Qizhe Xie, et al. ∙ 36 ∙ share

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AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

Machine learning research has advanced in multiple aspects, including mo...
03/06/2020 ∙ by Esteban Real, et al. ∙ 32 ∙ share

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Attention Augmented Convolutional Networks

Convolutional networks have been the paradigm of choice in many computer...
04/22/2019 ∙ by Irwan Bello, et al. ∙ 30 ∙ share

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RandAugment: Practical data augmentation with no separate search

Recent work has shown that data augmentation has the potential to signif...
09/30/2019 ∙ by Ekin D. Cubuk, et al. ∙ 26 ∙ share

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Adversarial Examples Improve Image Recognition

Adversarial examples are commonly viewed as a threat to ConvNets. Here w...
11/21/2019 ∙ by Cihang Xie, et al. ∙ 24 ∙ share

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Unsupervised Data Augmentation

Despite its success, deep learning still needs large labeled datasets to...
04/29/2019 ∙ by Qizhe Xie, et al. ∙ 18 ∙ share

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SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

We present SpecAugment, a simple data augmentation method for speech rec...
04/18/2019 ∙ by Daniel S. Park, et al. ∙ 16 ∙ share

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Towards a Human-like Open-Domain Chatbot

We present Meena, a multi-turn open-domain chatbot trained end-to-end on...
01/27/2020 ∙ by Daniel Adiwardana, et al. ∙ 16 ∙ share

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Smooth Adversarial Training

It is commonly believed that networks cannot be both accurate and robust...
06/25/2020 ∙ by Cihang Xie, et al. ∙ 13 ∙ share

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DropBlock: A regularization method for convolutional networks

Deep neural networks often work well when they are over-parameterized an...
10/30/2018 ∙ by Golnaz Ghiasi, et al. ∙ 12 ∙ share

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Saccader: Improving Accuracy of Hard Attention Models for Vision

Although deep convolutional neural networks achieve state-of-the-art per...
08/20/2019 ∙ by Gamaleldin F. Elsayed, et al. ∙ 9 ∙ share

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Evolving Normalization-Activation Layers

Normalization layers and activation functions are critical components in...
04/06/2020 ∙ by Hanxiao Liu, et al. ∙ 9 ∙ share

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Chip Placement with Deep Reinforcement Learning

In this work, we present a learning-based approach to chip placement, on...
04/22/2020 ∙ by Azalia Mirhoseini, et al. ∙ 9 ∙ share

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Learning Data Augmentation Strategies for Object Detection

Data augmentation is a critical component of training deep learning mode...
06/26/2019 ∙ by Barret Zoph, et al. ∙ 4 ∙ share

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Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Transformer networks have a potential of learning longer-term dependency...
01/09/2019 ∙ by Zihang Dai, et al. ∙ 2 ∙ share

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Selfie: Self-supervised Pretraining for Image Embedding

We introduce a pretraining technique called Selfie, which stands for SEL...
06/07/2019 ∙ by Trieu H. Trinh, et al. ∙ 2 ∙ share

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Improving 3D Object Detection through Progressive Population Based Augmentation

Data augmentation has been widely adopted for object detection in 3D poi...
04/02/2020 ∙ by Shuyang Cheng, et al. ∙ 1 ∙ share

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Building high-level features using large scale unsupervised learning

We consider the problem of building high-level, class-specific feature d...
12/29/2011 ∙ by Quoc V. Le, et al. ∙ 0 ∙ share

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Addressing the Rare Word Problem in Neural Machine Translation

Neural Machine Translation (NMT) is a new approach to machine translatio...
10/30/2014 ∙ by Minh-Thang Luong, et al. ∙ 0 ∙ share

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Exploiting Similarities among Languages for Machine Translation

Dictionaries and phrase tables are the basis of modern statistical machi...
09/17/2013 ∙ by Tomas Mikolov, et al. ∙ 0 ∙ share

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Sequence to Sequence Learning with Neural Networks

Deep Neural Networks (DNNs) are powerful models that have achieved excel...
09/10/2014 ∙ by Ilya Sutskever, et al. ∙ 0 ∙ share

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Intriguing Properties of Adversarial Examples

It is becoming increasingly clear that many machine learning classifiers...
11/08/2017 ∙ by Ekin D. Cubuk, et al. ∙ 0 ∙ share

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Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?

Deep reinforcement learning has achieved many recent successes, but our ...
11/07/2017 ∙ by Maithra Raghu, et al. ∙ 0 ∙ share

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A Bayesian Perspective on Generalization and Stochastic Gradient Descent

This paper tackles two related questions at the heart of machine learnin...
10/17/2017 ∙ by Samuel L. Smith, et al. ∙ 0 ∙ share

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Neural Optimizer Search with Reinforcement Learning

We present an approach to automate the process of discovering optimizati...
09/21/2017 ∙ by Irwan Bello, et al. ∙ 0 ∙ share

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Device Placement Optimization with Reinforcement Learning

The past few years have witnessed a growth in size and computational req...
06/13/2017 ∙ by Azalia Mirhoseini, et al. ∙ 0 ∙ share

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A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Learning long term dependencies in recurrent networks is difficult due t...
04/03/2015 ∙ by Quoc V. Le, et al. ∙ 0 ∙ share

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Learning Transferable Architectures for Scalable Image Recognition

Developing neural network image classification models often requires sig...
07/21/2017 ∙ by Barret Zoph, et al. ∙ 0 ∙ share

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Neural Combinatorial Optimization with Reinforcement Learning

This paper presents a framework to tackle combinatorial optimization pro...
11/29/2016 ∙ by Irwan Bello, et al. ∙ 0 ∙ share

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Neural Architecture Search with Reinforcement Learning

Neural networks are powerful and flexible models that work well for many...
11/05/2016 ∙ by Barret Zoph, et al. ∙ 0 ∙ share

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

Neural Machine Translation (NMT) is an end-to-end learning approach for ...
09/26/2016 ∙ by Yonghui Wu, et al. ∙ 0 ∙ share

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Distributed Representations of Sentences and Documents

Many machine learning algorithms require the input to be represented as ...
05/16/2014 ∙ by Quoc V. Le, et al. ∙ 0 ∙ share

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Adding Gradient Noise Improves Learning for Very Deep Networks

Deep feedforward and recurrent networks have achieved impressive results...
11/21/2015 ∙ by Arvind Neelakantan, et al. ∙ 0 ∙ share

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Learning Graph Matching

As a fundamental problem in pattern recognition, graph matching has appl...
06/17/2008 ∙ by Tibério S. Caetano, et al. ∙ 0 ∙ share

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Learning to Skim Text

Recurrent Neural Networks are showing much promise in many sub-areas of ...
04/23/2017 ∙ by Adams Wei Yu, et al. ∙ 0 ∙ share

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Learning a Natural Language Interface with Neural Programmer

Learning a natural language interface for database tables is a challengi...
11/28/2016 ∙ by Arvind Neelakantan, et al. ∙ 0 ∙ share

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Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

We propose a simple solution to use a single Neural Machine Translation ...
11/14/2016 ∙ by Melvin Johnson, et al. ∙ 0 ∙ share

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Unsupervised Pretraining for Sequence to Sequence Learning

Sequence to sequence models are successful tools for supervised sequence...
11/08/2016 ∙ by Prajit Ramachandran, et al. ∙ 0 ∙ share

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Multi-task Sequence to Sequence Learning

Sequence to sequence learning has recently emerged as a new paradigm in ...
11/19/2015 ∙ by Minh-Thang Luong, et al. ∙ 0 ∙ share

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A Neural Transducer

Sequence-to-sequence models have achieved impressive results on various ...
11/16/2015 ∙ by Navdeep Jaitly, et al. ∙ 0 ∙ share

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Neural Programmer: Inducing Latent Programs with Gradient Descent

Deep neural networks have achieved impressive supervised classification ...
11/16/2015 ∙ by Arvind Neelakantan, et al. ∙ 0 ∙ share

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Semi-supervised Sequence Learning

We present two approaches that use unlabeled data to improve sequence le...
11/04/2015 ∙ by Andrew M. Dai, et al. ∙ 0 ∙ share

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Listen, Attend and Spell

We present Listen, Attend and Spell (LAS), a neural network that learns ...
08/05/2015 ∙ by William Chan, et al. ∙ 0 ∙ share

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Document Embedding with Paragraph Vectors

Paragraph Vectors has been recently proposed as an unsupervised method f...
07/29/2015 ∙ by Andrew M. Dai, et al. ∙ 0 ∙ share

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Searching for Activation Functions

The choice of activation functions in deep networks has a significant ef...
10/16/2017 ∙ by Prajit Ramachandran, et al. ∙ 0 ∙ share

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Don't Decay the Learning Rate, Increase the Batch Size

It is common practice to decay the learning rate. Here we show one can u...
11/01/2017 ∙ by Samuel L. Smith, et al. ∙ 0 ∙ share

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Regularized Evolution for Image Classifier Architecture Search

The effort devoted to hand-crafting image classifiers has motivated the ...
02/05/2018 ∙ by Esteban Real, et al. ∙ 0 ∙ share

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Efficient Neural Architecture Search via Parameters Sharing

We propose Efficient Neural Architecture Search (ENAS), a fast and inexp...
02/09/2018 ∙ by Hieu Pham, et al. ∙ 0 ∙ share

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Quoc V. Le

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