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

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

In deep learning, models typically reuse the same parameters for all inp...
01/11/2021 ∙ by William Fedus, et al. ∙ 0 ∙ share

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Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

Building instance segmentation models that are data-efficient and can ha...
12/13/2020 ∙ by Golnaz Ghiasi, et al. ∙ 17 ∙ share

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Does Data Augmentation Benefit from Split BatchNorms

Data augmentation has emerged as a powerful technique for improving the ...
10/15/2020 ∙ by Amil Merchant, et al. ∙ 21 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Barret Zoph

Featured Co-authors

Yu Zhang

Alan Yuille

Li Fei-Fei

Quoc V. Le

Kevin Murphy

Yang Song

Jonathon Shlens

Chung-Cheng Chiu

Balaji Lakshminarayanan

Noam Shazeer

Liang-Chieh Chen

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

Does Data Augmentation Benefit from Split BatchNorms

Rethinking Pre-training and Self-training

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

Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Improving 3D Object Detection through Progressive Population Based Augmentation

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

RandAugment: Practical data augmentation with no separate search

Learning Data Augmentation Strategies for Object Detection

Attention Augmented Convolutional Networks

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

Backprop Evolution

AutoAugment: Learning Augmentation Policies from Data

Efficient Neural Architecture Search via Parameter Sharing

Efficient Neural Architecture Search via Parameters Sharing

Progressive Neural Architecture Search

Intriguing Properties of Adversarial Examples

Searching for Activation Functions

Neural Optimizer Search with Reinforcement Learning

Learning Transferable Architectures for Scalable Image Recognition

Neural Architecture Search with Reinforcement Learning

Transfer Learning for Low-Resource Neural Machine Translation

Multi-Source Neural Translation