Barret Zoph

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Google ∙ USC Information Sciences Institute

Research Scientist at Google Inc.- Google Brain Team since 2016, Computer Scientist at Information Sciences Institute 2016

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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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Modern deep neural networks can achieve high accuracy when the training ...
12/05/2019 ∙ by Dan Hendrycks, et al. ∙ 21 ∙ 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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Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Supervised learning in large discriminative models is a mainstay for mod...
05/20/2020 ∙ by Liang-Chieh Chen, et al. ∙ 14 ∙ share

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Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Supervised learning in large discriminative models is a mainstay for mod...
05/20/2020 ∙ by Liang-Chieh Chen, et al. ∙ 7 ∙ share

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Backprop Evolution

The back-propagation algorithm is the cornerstone of deep learning. Desp...
08/08/2018 ∙ by Maximilian Alber, et al. ∙ 4 ∙ 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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Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

The design of neural network architectures is an important component for...
09/11/2018 ∙ by Liang-Chieh Chen, 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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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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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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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 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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Progressive Neural Architecture Search

We propose a method for learning CNN structures that is more efficient t...
12/02/2017 ∙ by Chenxi Liu, et al. ∙ 0 ∙ share

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Transfer Learning for Low-Resource Neural Machine Translation

The encoder-decoder framework for neural machine translation (NMT) has b...
04/08/2016 ∙ by Barret Zoph, et al. ∙ 0 ∙ share

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Multi-Source Neural Translation

We build a multi-source machine translation model and train it to maximi...
01/05/2016 ∙ by Barret Zoph, 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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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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Efficient Neural Architecture Search via Parameter 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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AutoAugment: Learning Augmentation Policies from Data

In this paper, we take a closer look at data augmentation for images, an...
05/24/2018 ∙ by Ekin D. Cubuk, et al. ∙ 0 ∙ share

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Rethinking Pre-training and Self-training

Pre-training is a dominant paradigm in computer vision. For example, sup...
06/11/2020 ∙ by Barret Zoph, et al. ∙ 0 ∙ share

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Barret Zoph

Featured Co-authors

Yu Zhang

Li Fei-Fei

Alan Yuille

Quoc V. Le

Kevin Murphy

Yang Song

Jonathon Shlens

Balaji Lakshminarayanan

Liang-Chieh Chen

Chung-Cheng Chiu

Hartwig Adam

Attention Augmented Convolutional Networks

RandAugment: Practical data augmentation with no separate search

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Backprop Evolution

Learning Data Augmentation Strategies for Object Detection

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

Improving 3D Object Detection through Progressive Population Based Augmentation

Intriguing Properties of Adversarial Examples

Neural Optimizer Search with Reinforcement Learning

Learning Transferable Architectures for Scalable Image Recognition

Neural Architecture Search with Reinforcement Learning

Progressive Neural Architecture Search

Transfer Learning for Low-Resource Neural Machine Translation

Multi-Source Neural Translation

Searching for Activation Functions

Efficient Neural Architecture Search via Parameters Sharing

Efficient Neural Architecture Search via Parameter Sharing

AutoAugment: Learning Augmentation Policies from Data

Rethinking Pre-training and Self-training