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The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS).
Correlated and uncorrelated fitness landscapes and how to tell the difference
Edward Weinberger · 1990
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Random forests
Leo Breiman · 2001
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Exploring network structure, dynamics, and function using networkx
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
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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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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Learning curve prediction with bayesian neural networks
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, and Frank Hutter · 2017
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Neural network intelligence
Microsoft · 2017
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Deeparchitect: Automatically designing and training deep architectures
Renato Negrinho and Geoff Gordon · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
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Accelerating neural architecture search using performance prediction
Bowen Baker, Otkrist Gupta, Ramesh Raskar, and Nikhil Naik · 2018
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A tutorial on bayesian optimization
Peter I Frazier · 2018
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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
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Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2018
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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
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Searching for a robust neural architecture in four gpu hours
Xuanyi Dong and Yi Yang · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
Cited alongside, same era.
Training a single ai model can emit as much carbon as five cars in their lifetimes
Karen Hao · 2019
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 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.
Tackling climate change with machine learning
David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, et al · 2019
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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Weight-sharing neural architecture search: A battle to shrink the optimization gap
Lingxi Xie, Xin Chen, Kaifeng Bi, Longhui Wei, Yuhui Xu, Zhengsu Chen, Lanfei Wang, An Xiao, Jianlong Chang, Xiaopeng Zhang, et al · 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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Cited alongside, same era.
The evolved transformer
David R. So, Chen Liang, and Quoc V. Le · 2019
Cited alongside, same era.
Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2019
Cited alongside, same era.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Cited alongside, same era.
Densely connected search space for more flexible neural architecture search
Jiemin Fang, Yuzhu Sun, Qian Zhang, Yuan Li, Wenyu Liu, and Xinggang Wang · 2020
Cited alongside, same era.
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2020
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Hpo-b: A large-scale reproducible benchmark for black-box hpo based on openml
Sebastian Pineda Arango, Hadi S Jomaa, Martin Wistuba, and Josif Grabocka · 2021
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Drnas: Dirichlet neural architecture search
Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, and Cho-Jui Hsieh · 2021
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Learning versatile neural architectures by propagating network codes
Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang, and Ping Luo · 2021
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Nats-bench: Benchmarking nas algorithms for architecture topology and size
Xuanyi Dong, Lu Liu, Katarzyna Musial, and Bogdan Gabrys · 2021
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Transnas-bench-101: Improving transferability and generalizability of cross-task neural architecture search
Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, and Zhenguo Li · 2021
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Hpobench: A collection of reproducible multi-fidelity benchmark problems for hpo
Katharina Eggensperger, Philipp Müller, Neeratyoy Mallik, Matthias Feurer, René Sass, Aaron Klein, Noor Awad, Marius Lindauer, and Frank Hutter · 2021
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Nas-bench-asr: Reproducible neural architecture search for speech recognition
Abhinav Mehrotra, Alberto Gil C. P. Ramos, Sourav Bhattacharya, Łukasz Dudziak, Ravichander Vipperla, Thomas Chau, Mohamed S Abdelfattah, Samin Ishtiaq, and Nicholas Donald Lane · 2021
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Local search is a remarkably strong baseline for neural architecture search
T Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, and Peter AN Bosman · 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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Pyglove: Symbolic programming for automated machine learning
Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Hanxiao Liu, Gabriel Bender, Adam Kraft, Chen Liang, and Quoc V Le · 2021
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Prioritized architecture sampling with monto-carlo tree search
Xiu Su, Tao Huang, Yanxi Li, Shan You, Fei Wang, Chen Qian, Changshui Zhang, and Chang Xu · 2021
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Nas-bench-360: Benchmarking diverse tasks for neural architecture search
Renbo Tu, Mikhail Khodak, Nicholas Carl Roberts, Nina Balcan, and Ameet Talwalkar · 2021
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Nas-bench-x11 and the power of learning curves
Shen Yan, Colin White, Yash Savani, and Frank Hutter · 2021
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