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Over the past half-decade, many methods have been considered for neural architecture search (NAS).
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
Harold J Kushner · 1964
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On bayesian methods for seeking the extremum
Jonas Močkus · 1975
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Designing neural networks using genetic algorithms with graph generation system
Hiroaki Kitano · 1990
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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The parallel bayesian optimization algorithm
Jiří Očenášek and Josef Schwarz · 2000
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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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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2009
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Earlier work this paper cites.
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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Scalable bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
Earlier work this paper cites.
Ensemble of deep convolutional neural networks for prognosis of ischemic stroke
Youngwon Choi, Yongchan Kwon, Hanbyul Lee, Beom Joon Kim, Myunghee Cho Paik, and Joong-Ho Won · 2016
Earlier work this paper cites.
Batch bayesian optimization via local penalization
Javier González, Zhenwen Dai, Philipp Hennig, and Neil Lawrence · 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
Earlier work this paper cites.
Bayesian optimization with robust bayesian neural networks
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, and Frank Hutter · 2016
Earlier work this paper cites.
Accelerating neural architecture search using performance prediction
Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2017
Earlier work this paper cites.
Peephole: Predicting network performance before training
Boyang Deng, Junjie Yan, and Dahua Lin · 2017
Earlier work this paper cites.
Google vizier: A service for black-box optimization
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D Sculley · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Learning curve prediction with bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
Cited alongside, same era.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
Cited alongside, same era.
Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
Cited alongside, same era.
A tutorial on bayesian optimization
Peter I Frazier · 2018
Cited alongside, same era.
Deep neural architecture search with deep graph bayesian optimization
Lizheng Ma, Jiaxu Cui, and Bo Yang · 2019
Closest in time.
Probo: a framework for using probabilistic programming in bayesian optimization
Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, and Eric Xing · 2019
Closest in time.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Closest in time.
Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
Closest in time.
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
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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
Cited alongside, same era.
Auto-keras: Efficient neural architecture search with network morphism
Haifeng Jin, Qingquan Song, and Xia Hu · 2018
Cited alongside, same era.
Parallelised bayesian optimisation via thompson sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, and Barnabás Póczos · 2018
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric P Xing · 2018
Cited alongside, same era.
Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
Cited alongside, same era.
Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
Cited alongside, same era.
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
Cited alongside, same era.
Closest in time.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado · 2019
Closest in time.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
Closest in time.
Sample-efficient neural architecture search by learning action space
Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, and Yuandong Tian · 2019
Closest in time.
Neural predictor for neural architecture search
Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, and Pieter-Jan Kindermans · 2019
Closest in time.
Pc-darts: Partial channel connections for memory-efficient architecture search
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong · 2019
Closest in time.
Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2019
Closest in time.
D-vae: A variational autoencoder for directed acyclic graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, and Yixin Chen · 2019
Closest in time.
Bayesnas: A bayesian approach for neural architecture search
Hongpeng Zhou, Minghao Yang, Jun Wang, and Wei Pan · 2019
Closest in time.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Closest in time.
Neural architecture search with reinforce and masked attention autoregressive density estimators
Chepuri Shri Krishna, Ashish Gupta, Himanshu Rai, and Swarnim Narayan · 2020
Closest in time.
Optimal transport kernels for sequential and parallel neural architecture search
Vu Nguyen, Tam Le, Makoto Yamada, and Michael A Osborne · 2020
Closest in time.
Neural architecture search using bayesian optimisation with weisfeiler-lehman kernel
Binxin Ru, Xingchen Wan, Xiaowen Dong, and Michael Osborne · 2020
Closest in time.
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
Closest in time.
Methods for comparing uncertainty quantifications for material property predictions
Kevin Tran, Willie Neiswanger, Junwoong Yoon, Qingyang Zhang, Eric Xing, and Zachary W Ulissi · 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.
A study on encodings for neural architecture search
Colin White, Willie Neiswanger, Sam Nolen, and Yash Savani · 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.
Does unsupervised architecture representation learning help neural architecture search?
Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, and Mi Zhang · 2020
Closest in time.
Nas evaluation is frustratingly hard
Antoine Yang, Pedro M Esperança, and Fabio M Carlucci · 2020
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Neural ensemble search for performant and calibrated predictions
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, and Yee Whye Teh · 2020
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Neural predictor for neural architecture search
Yuge Zhang · 2020
Closest in time.