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Early methods in the rapidly developing field of neural architecture search (NAS) required fully training thousands of neural networks.
Designing neural networks using genetic algorithms with graph generation system
Hiroaki Kitano · 1990
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Classification and regression by randomforest
Andy Liaw, Matthew Wiener, et al · 2002
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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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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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A unifying view of sparse approximate gaussian process regression
Joaquin Quiñonero Candela and Carl Edward Rasmussen · 2005
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Variational learning of inducing variables in sparse gaussian processes
Michalis Titsias · 2009
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Recurrent neural network based language model
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces
Kevin Swersky, David Duvenaud, Jasper Snoek, Frank Hutter, and Michael Osborne · 2013
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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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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
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Understanding probabilistic sparse gaussian process approximations
Matthias Bauer, Mark van der Wilk, and Carl Edward Rasmussen · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 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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Bayesian optimization with robust Bayesian neural networks
Jost Tobias Springenberg, Aaron Klein, Stefan Falkner, and Frank Hutter · 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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Speeding up hyper-parameter optimization by extrapolation of learning curves using previous builds
Akshay Chandrashekaran and Ian R Lane · 2017
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A downsampled variant of imagenet as an alternative to the CIFAR datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Learning curve prediction with Bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter · 2017
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Smac v3: Algorithm configuration in python
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, Stefan Falkner, André Biedenkapp, and Frank Hutter · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
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Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 2018
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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
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
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2018
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Evolutionary-neural hybrid agents for architecture search
Krzysztof Maziarz, Andrey Khorlin, Quentin de Laroussilhe, and Andrea Gesmundo · 2018
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A system for massively parallel hyperparameter tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar · 2020
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Neural architecture search with gbdt
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2020
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Semi-supervised neural architecture search
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, and Tie-Yan Liu · 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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A surgery of the neural architecture evaluators
Xuefei Ning, Wenshuo Li, Zixuan Zhou, Tianchen Zhao, Yin Zheng, Shuang Liang, Huazhong Yang, and Yu Wang · 2020
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Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
Cited alongside, same era.
Faster gaze prediction with dense networks and fisher pruning
Lucas Theis, Iryna Korshunova, Alykhan Tejani, and Ferenc Huszár · 2018
Cited alongside, same era.
Alphax: exploring neural architectures with deep neural networks and monte carlo tree search
Linnan Wang, Yiyang Zhao, Yuu Jinnai, and Rodrigo Fonseca · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Cited alongside, same era.
Pyro: Deep universal probabilistic programming
Eli Bingham, Jonathan P Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, and Noah D Goodman · 2019
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
Cited alongside, same era.
Searching for a robust neural architecture in four gpu hours
Xuanyi Dong and Yi Yang · 2019
Cited alongside, same era.
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A generic graph-based neural architecture encoding scheme for predictor-based nas
Xuefei Ning, Yin Zheng, Tianchen Zhao, Yu Wang, and Huazhong Yang · 2020
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Revisiting the train loss: an efficient performance estimator for neural architecture search
Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, and Yarin Gal · 2020
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Neural architecture search using Bayesian optimisation with weisfeiler-lehman kernel
Binxin Ru, Xingchen Wan, Xiaowen Dong, and Michael Osborne · 2020
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Naslib: a modular and flexible neural architecture search library, 2020
Michael Ruchte, Arber Zela, Julien Siems, Josif Grabocka, and Frank Hutter · 2020
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Bridging the gap between sample-based and one-shot neural architecture search with bonas
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James Kwok, and Tong Zhang · 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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Yanan Sun, Xian Sun, Yuhan Fang, and Gary Yen · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel LK Yamins, and Surya Ganguli · 2020
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Npenas: Neural predictor guided evolution for neural architecture search
Chen Wei, Chuang Niu, Yiping Tang, and Jimin Liang · 2020
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A study on encodings for neural architecture search
Colin White, Willie Neiswanger, Sam Nolen, and Yash Savani · 2020
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Nas evaluation is frustratingly hard
Antoine Yang, Pedro M Esperança, and Fabio M Carlucci · 2020
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How to train your super-net: An analysis of training heuristics in weight-sharing nas
Kaicheng Yu, Rene Ranftl, and Mathieu Salzmann · 2020
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Understanding and robustifying differentiable architecture search
Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, and Frank Hutter · 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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Neural predictor for neural architecture search
Yuge Zhang · 2020
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Deeper insights into weight sharing in neural architecture search
Yuge Zhang, Zejun Lin, Junyang Jiang, Quanlu Zhang, Yujing Wang, Hui Xue, Chen Zhang, and Yaming Yang · 2020
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Econas: Finding proxies for economical neural architecture search
Dongzhan Zhou, Xinchi Zhou, Wenwei Zhang, Chen Change Loy, Shuai Yi, Xuesen Zhang, and Wanli Ouyang · 2020
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Zero-cost proxies for lightweight nas
Mohamed S Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas Donald Lane · 2021
Closest in time.
Geometry-aware gradient algorithms for neural architecture search
Liam Li, Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2021
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Carbon emissions and large neural network training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean · 2021
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Bananas: Bayesian optimization with neural architectures for neural architecture search
Colin White, Willie Neiswanger, and Yash Savani · 2021
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