Barret Zoph

28.1k total citations · 5 hit papers
20 papers, 4.9k citations indexed

About

Barret Zoph is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Barret Zoph has authored 20 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 2 papers in Information Systems. Recurrent topics in Barret Zoph's work include Advanced Neural Network Applications (8 papers), Natural Language Processing Techniques (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Barret Zoph is often cited by papers focused on Advanced Neural Network Applications (8 papers), Natural Language Processing Techniques (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Barret Zoph collaborates with scholars based in United States and Germany. Barret Zoph's co-authors include Quoc V. Le, Ekin D. Cubuk, Vijay Vasudevan, Yu Zhang, Daniel Park, Chung‐Cheng Chiu, William Chan, Jeff Dean, Hieu Pham and Melody Y. Guan and has published in prestigious journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Barret Zoph

19 papers receiving 4.7k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

2019 2.1k citations Daniel Park, William Chan et al. arXiv (Cornell University) profile →
AutoAugment: Learning Augmentation Strategies From Data

2019 1.5k citations Ekin D. Cubuk, Barret Zoph et al. profile →
Efficient Neural Architecture Search via Parameters Sharing

2018 598 citations Hieu Pham, Melody Y. Guan et al. International Conference on Machine Learning profile →
Neural Architecture Search with Reinforcement Learning

2016 336 citations Barret Zoph, Quoc V. Le arXiv (Cornell University) profile →
Understanding and Simplifying One-Shot Architecture Search

2018 234 citations Gabriel Bender, Pieter-Jan Kindermans et al. International Conference on Machine Learning profile →

Peers

Countries citing papers authored by Barret Zoph

Since Specialization

Citations

This map shows the geographic impact of Barret Zoph's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Barret Zoph with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Barret Zoph more than expected).

Fields of papers citing papers by Barret Zoph

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Barret Zoph. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Barret Zoph. The network helps show where Barret Zoph may publish in the future.

Co-authorship network of co-authors of Barret Zoph

This figure shows the co-authorship network connecting the top 25 collaborators of Barret Zoph. A scholar is included among the top collaborators of Barret Zoph based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Barret Zoph. Barret Zoph is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	A Pretrainer’s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity 2024 ·Shayne Longpre, (unknown), Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Lee, Kevin Robinson, David Mimno, Daphne Ippolito	11
2	A Review of Sparse Expert Models in Deep Learning 2022 ·arXiv (Cornell University) ·William Fedus, Jeff Dean, Barret Zoph	25
3	Designing Effective Sparse Expert Models 2022 ·Barret Zoph	26
4	Revisiting ResNets: Improved Training and Scaling Strategies 2021 ·Neural Information Processing Systems ·Irwan Bello, William Fedus, Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph	2
5	Multi-Task Self-Training for Learning General Representations 2021 ·2021 IEEE/CVF International Conference on Computer Vision (ICCV) ·Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin	40
6	Revisiting ResNets: Improved Training Methodologies and Scaling Principles 2021 ·Irwan Bello, (unknown), Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas, Tsung-Yi Lin, (unknown), Barret Zoph	1
7	Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation. 2020 ·arXiv (Cornell University) ·Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens	2
8	Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation 2020 ·arXiv (Cornell University) ·Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens	5
9	Rethinking Pre-training and Self-training 2020 ·arXiv (Cornell University) ·Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, Quoc V. Le	21
10	AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty 2020 ·International Conference on Learning Representations ·Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan	61
11	AutoAugment: Learning Augmentation Strategies From Data breakdown → 2019 ·Ekin D. Cubuk, Barret Zoph, (unknown), Vijay Vasudevan, Quoc V. Le	1464
12	SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition breakdown → 2019 ·arXiv (Cornell University) ·Daniel Park, William Chan, Yu Zhang, Chung‐Cheng Chiu, Barret Zoph, Ekin D. Cubuk, Quoc V. Le	2055
13	Faster Discovery of Neural Architectures by Searching for Paths in a Large Model 2018 ·International Conference on Learning Representations ·Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean	6
14	Understanding and Simplifying One-Shot Architecture Search breakdown → 2018 ·International Conference on Machine Learning ·Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, Quoc V. Le	234
15	Efficient Neural Architecture Search via Parameters Sharing breakdown → 2018 ·International Conference on Machine Learning ·Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, Jeff Dean	598
16	EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS 2018 ·Minh-Thang Luong, David Dohan, Adams Wei Yu, Quoc V. Le, Barret Zoph, Vijay Vasudevan	0
17	Neural optimizer search with reinforcement learning 2017 ·International Conference on Machine Learning ·Irwan Bello, Barret Zoph, Vijay Vasudevan, Quoc V. Le	37
18	Neural Architecture Search with Reinforcement Learning breakdown → 2016 ·arXiv (Cornell University) ·Barret Zoph, Quoc V. Le	336
19	Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies 2016 ·Barret Zoph, Ashish Vaswani, Jonathan May, Kevin Knight	18
20	How Much Information Does a Human Translator Add to the Original? 2015 ·Barret Zoph, Marjan Ghazvininejad, Kevin Knight	3

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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