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Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS).
Self organizing neural networks for the identification problem
Manoel Tenorio and Wei-Tsih Lee · 1988
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Designing neural networks using genetic algorithms
Geoffrey F Miller, Peter M Todd, and Shailesh U Hegde · 1989
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Sturges’ rule
David W Scott · 2009
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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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
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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
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Shift: A zero flop, zero parameter alternative to spatial convolutions
Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, and Kurt Keutzer · 2018
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Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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Training a single ai model can emit as much carbon as five cars in their lifetimes
Karen Hao · 2019
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SNIP: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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A survey on neural architecture search
Martin Wistuba, Ambrish Rawat, and Tejaswini Pedapati · 2019
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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
Earlier work this paper cites.
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.
Brp-nas: Prediction-based nas using gcns
Lukasz Dudziak, Thomas Chau, Mohamed Abdelfattah, Royson Lee, Hyeji Kim, and Nicholas Lane · 2020
Cited alongside, same era.
Towards nngp-guided neural architecture search
Daniel S Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, and Jascha Sohl-Dickstein · 2020
Cited alongside, same era.
Naslib: A modular and flexible neural architecture search library
Michael Ruchte, Arber Zela, Julien Siems, Josif Grabocka, and Frank Hutter · 2020
Cited alongside, same era.
Epe-nas: Efficient performance estimation without training for neural architecture search
Vasco Lopes, Saeid Alirezazadeh, and Luís A Alexandre · 2021
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Neural architecture search without training
Joe Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley · 2021
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Evaluating efficient performance estimators of neural architectures
Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, and Yu Wang · 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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Speedy performance estimation for neural architecture search
Robin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, and Yarin Gal · 2021
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Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James Kwok, and Tong Zhang · 2020
Cited alongside, same era.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli · 2020
Cited alongside, same era.
Blockswap: Fisher-guided block substitution for network compression on a budget
Jack Turner, Elliot J Crowley, Michael O’Boyle, Amos Storkey, and Gavin Gray · 2020
Cited alongside, same era.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Cited alongside, same era.
A study on encodings for neural architecture search
Colin White, Willie Neiswanger, Sam Nolen, and Yash Savani · 2020
Cited alongside, same era.
Zero-cost proxies for lightweight nas
Mohamed S Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas Donald Lane · 2021
Cited alongside, same era.
Nas-bench-zero: A large scale dataset for understanding zero-shot neural architecture search
Hanlin Chen, Ming Lin, Xiuyu Sun, and Hao Li · 2021
Cited alongside, same era.
Yu Shen, Yang Li, Jian Zheng, Wentao Zhang, Peng Yao, Jixiang Li, Sen Yang, Ji Liu, and Cui Bin · 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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Bananas: Bayesian optimization with neural architectures for neural architecture search
Colin White, Willie Neiswanger, and Yash Savani · 2021
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How powerful are performance predictors in neural architecture search?
Colin White, Arber Zela, Robin Ru, Yang Liu, and Frank Hutter · 2021
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Zero-cost proxies meet differentiable architecture search
Lichuan Xiang, Łukasz Dudziak, Mohamed S Abdelfattah, Thomas Chau, Nicholas D Lane, and Hongkai Wen · 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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Litetransformersearch: Training-free on-device search for efficient autoregressive language models
Mojan Javaheripi, Shital Shah, Subhabrata Mukherjee, Tomasz L Religa, Caio CT Mendes, Gustavo H de Rosa, Sebastien Bubeck, Farinaz Koushanfar, and Debadeepta Dey · 2022
Closest in time.
Nas-bench-suite: Nas evaluation is (now) surprisingly easy
Yash Mehta, Colin White, Arber Zela, Arjun Krishnakumar, Guri Zabergja, Shakiba Moradian, Mahmoud Safari, Kaicheng Yu, and Frank Hutter · 2022
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Nasi: Label-and data-agnostic neural architecture search at initialization
Yao Shu, Shaofeng Cai, Zhongxiang Dai, Beng Chin Ooi, and Bryan Kian Hsiang Low · 2022
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Unifying and boosting gradient-based training-free neural architecture search
Yao Shu, Zhongxiang Dai, Zhaoxuan Wu, and Kian Hsiang Low · 2022
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Npenas: Neural predictor guided evolution for neural architecture search
Chen Wei, Chuang Niu, Yiping Tang, Yue Wang, Haihong Hu, and Jimin Liang · 2022
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A deeper look at zero-cost proxies for lightweight nas
Colin White, Mikhail Khodak, Renbo Tu, Shital Shah, Sébastien Bubeck, and Debadeepta Dey · 2022
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Surrogate nas benchmarks: Going beyond the limited search spaces of tabular nas benchmarks
Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2022
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Training-free transformer architecture search
Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, and Rongrong Ji · 2022
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