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Neural architecture search (NAS) has been extensively studied in the past few years.
Designing neural networks using genetic algorithms with graph generation system
Hiroaki Kitano · 1990
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Good lower and upper bounds on binomial coefficients
Pantelimon Stanica · 2001
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Evolving neural networks through augmenting topologies
Kenneth O Stanley and Risto Miikkulainen · 2002
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A hypercube-based indirect encoding for evolving large-scale neural networks
KO Stanley, DB D’Ambrosio, and J Gauci · 2009
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
Earlier work this paper cites.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
Earlier work this paper cites.
It’s time to do something: Mitigating the negative impacts of computing through a change to the peer review process
Brent Hecht, Lauren Wilcox, Jeffrey P Bigham, Johannes Schöning, Ehsan Hoque, Jason Ernst, Yonatan Bisk, Luigi De Russis, Lana Yarosh, Bushra Anjum, Danish Contractor, and Cathy Wu · 2018
Earlier work this paper cites.
Auto-keras: Efficient neural architecture search with network morphism
Haifeng Jin, Qingquan Song, and Xia Hu · 2018
Earlier work this paper cites.
Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric P Xing · 2018
Earlier work this paper cites.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Earlier work this paper cites.
Evolutionary-neural hybrid agents for architecture search
Krzysztof Maziarz, Andrey Khorlin, Quentin de Laroussilhe, and Andrea Gesmundo · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
Cited alongside, same era.
A graph-based encoding for evolutionary convolutional neural network architecture design
William Irwin-Harris, Yanan Sun, Bing Xue, and Mengjie Zhang · 2019
Cited alongside, same era.
Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
Cited alongside, same era.
Best practices for scientific research on neural architecture search
Neural predictor for neural architecture search
Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, and Pieter-Jan Kindermans · 2019
Later among the works it cites.
Bananas: Bayesian optimization with neural architectures for neural architecture search
Colin White, Willie Neiswanger, and Yash Savani · 2019
Later among the works it cites.
Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2019
Later among the works it cites.
D-vae: A variational autoencoder for directed acyclic graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen · 2019
Later among the works it cites.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Closest in time.
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Marius Lindauer and Frank Hutter · 2019
Cited alongside, same era.
Probo: a framework for using probabilistic programming in bayesian optimization
Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, and Eric Xing · 2019
Cited alongside, same era.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Cited alongside, same era.
Multi-objective neural architecture search via predictive network performance optimization
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T Kwok, and Tong Zhang · 2019
Cited alongside, same era.
Evolving deep convolutional neural networks for image classification
Yanan Sun, Bing Xue, Mengjie Zhang, and Gary G Yen · 2019
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Cited alongside, same era.
Optimization of deep neural networks: a survey and unified taxonomy
El-Ghazali Talbi · 2020
Closest in time.
Npenas: Neural predictor guided evolution for neural architecture search
Chen Wei, Chuang Niu, Yiping Tang, and Jimin Liang · 2020
Closest in time.
Local search is state of the art for nas benchmarks
Colin White, Sam Nolen, and Yash Savani · 2020
Closest in time.
Nas evaluation is frustratingly hard
Antoine Yang, Pedro M Esperança, and Fabio M Carlucci · 2020
Closest in time.