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Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS).
Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael C Mozer and Paul Smolensky · 1989
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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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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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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Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 2018
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Smash: One-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James Millar Ritchie, and Nicholas J Weston · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 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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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Faster gaze prediction with dense networks and fisher pruning
Lucas Theis, Iryna Korshunova, Alykhan Tejani, and Ferenc Huszár · 2018
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Overcoming multi-model forgetting
Yassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C Davison, Mathieu Salzmann, and Claudiu Musat · 2019
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ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 2019
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Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
Xiangxiang Chu, Bo Zhang, Ruijun Xu, and Jixiang Li · 2019
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One-shot neural architecture search via self-evaluated template network
Xuanyi Dong and Yi Yang · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, Frank Hutter, et al · 2019
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Nas-fpn: Learning scalable feature pyramid architecture for object detection
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2019
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Balanced one-shot neural architecture optimization
Renqian Luo, Tao Qin, and Enhong Chen · 2019
Cited alongside, same era.
Xnas: Neural architecture search with expert advice
Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, and Lihi Zelnik · 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.
Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
Cited alongside, same era.
Blockswap: Fisher-guided block substitution for network compression on a budget
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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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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Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 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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Cars: Continuous evolution for efficient neural architecture search
Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, and Chang Xu · 2020
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Jack Turner, Elliot J Crowley, Michael O’Boyle, Amos Storkey, and Gavin Gray · 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.
Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter · 2019
Cited alongside, same era.
Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2019
Cited alongside, same era.
Evolving search space for neural architecture search
Yuanzheng Ci, Chen Lin, Ming Sun, Boyu Chen, Hongwen Zhang, and Wanli Ouyang · 2020
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.
Powering one-shot topological nas with stabilized share-parameter proxy
Ronghao Guo, Chen Lin, Chuming Li, Keyu Tian, Ming Sun, Lu Sheng, and Junjie Yan · 2020
Cited alongside, same era.
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How to train your super-net: An analysis of training heuristics in weight-sharing NAS
Kaicheng Yu, René Ranftl, and Mathieu Salzmann · 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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Overcoming multi-model forgetting in one-shot nas with diversity maximization
Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, and Steven Su · 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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Zero-Cost Proxies for Lightweight NAS
Mohamed S. Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas D. Lane · 2021
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Neural architecture search on imagenet in four gpu hours: A theoretically inspired perspective
Wuyang Chen, Xinyu Gong, and Zhangyang Wang · 2021
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Zen-nas: A zero-shot nas for high-performance deep image recognition
Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin · 2021
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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
Joseph Mellor, Jack Turner, Amos Storkey, and Elliot J. Crowley · 2021
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Neural architecture search without training
Joseph Mellor, Jack Turner, Amos Storkey, and Elliot J. Crowley · 2021
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Speedy performance estimation for neural architecture search, 2021
Binxin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, and Yarin Gal · 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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