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Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Tobias Domhan, Jost Tobias Springenberg, and Frank Hutter · 2015
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Pac-bayesian theory meets bayesian inference
Pascal Germain, Francis Bach, Alexandre Lacoste, and Simon Lacoste-Julien · 2016
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 2016
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Fast Bayesian optimization of machine learning hyperparameters on large datasets
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, and Frank Hutter · 2016
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Learning curve prediction with bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter · 2016
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2016
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Accelerating neural architecture search using performance prediction
Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2017
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Algorithmic stability and hypothesis complexity
Tongliang Liu, Gábor Lugosi, Gergely Neu, and Dacheng Tao · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc Le · 2017
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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BOHB: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Cited alongside, same era.
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
Cited alongside, same era.
Generalization bounds of stochastic gradient descent for wide and deep neural networks
Yuan Cao and Quanquan Gu · 2019
Cited alongside, same era.
Progressive differentiable architecture search
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 2019
Cited alongside, same era.
Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
Xiangxiang Chu, Bo Zhang, Ruijun Xu, and Jixiang Li · 2019
Cited alongside, same era.
Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, and Samy Bengio · 2020
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The large learning rate phase of deep learning: the catapult mechanism
Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer, Jascha Sohl-Dickstein, and Guy Gur-Ari · 2020
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2020
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A Bayesian perspective on training speed and model selection
Clare Lyle, Lisa Schut, Binxin Ru, Mark van der Wilk, and Yarin Gal · 2020
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Neural architecture search without training
Joseph Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley · 2020
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Stanislav Fort, Paweł Krzysztof Nowak, Stanislaw Jastrzebski, and Srini Narayanan · 2019
Cited alongside, same era.
Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
Cited alongside, same era.
DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Cited alongside, same era.
Information-theoretic generalization bounds for sgld via data-dependent estimates
Jeffrey Negrea, Mahdi Haghifam, AI Element, Gintare K Dziugaite, Ashish Khisti, and Daniel M Roy · 2019
Cited alongside, same era.
On network design spaces for visual recognition
Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, and Piotr Dollár · 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.
Meta architecture search
Albert Shaw, Wei Wei, Weiyang Liu, Le Song, and Bo Dai · 2019
Cited alongside, same era.
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip HS Torr, and Puneet K Dokania · 2020
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Neural architecture generator optimization
Binxin Ru, Pedro Esperança, and Fabio Maria Carlucci · 2020
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Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2020
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NAS evaluation is frustratingly hard
Antoine Yang, Pedro M. Esperança, and Fabio M. Carlucci · 2020
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Evaluating the search phase of neural architecture search
Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2020
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Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
Arber Zela, Julien Siems, and Frank Hutter · 2020
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Theory-inspired path-regularized differential network architecture search
Pan Zhou, Caiming Xiong, Richard Socher, and Steven CH Hoi · 2020
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Zero-cost proxies for lightweight {nas}
Mohamed S Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas Donald Lane · 2021
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Dr{nas}: Dirichlet neural architecture search
Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, and Cho-Jui Hsieh · 2021
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Information-theoretic generalization bounds for stochastic gradient descent, 2021
Gergely Neu · 2021
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On the origin of implicit regularization in stochastic gradient descent
Samuel L. Smith, Benoit Dherin, David G. T. Barrett, and Soham De · 2021
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How powerful are performance predictors in neural architecture search?
Colin White, Arber Zela, Binxin Ru, Yang Liu, and Frank Hutter · 2021
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