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Recent state-of-the-art methods for neural architecture search (NAS) exploit gradient-based optimization by relaxing the problem into continuous optimization over architectures and shared-weights, a noisy process that remains poorly understood.
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
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Differentiable sparse coding
David M. Bradley and J. Andrew Bagnell · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevksy · 2009
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2011
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Proximal algorithms
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Stochastic block mirror descent methods for nonsmooth and stochastic optimization
Cong D. Dang and Guanghui Lan · 2015
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Adam: A method for stochastic optimization
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ImageNet large scale visual recognition challenge
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W. Taylor · 2017
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Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Prajit Ramachandran, Barret Zoph, and Quoc V. Le · 2017
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimilano Pontil · 2018
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Jie Hu, Li Shen, and Gang Sun · 2018
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An alternative view: When does SGD escape local minima?
Bobby Kleinberg, Yuanzhi Li, and Yang Yuan · 2018
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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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ShuffleNet V2: Practical guidelines for efficient CNN architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
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ShakeDrop regularization
Yoshihiro Yamada, Masakazu Iawmura, and Koichi Kise · 2018
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On the convergence rate of stochastic mirror descent for nonsmooth nonconvex optimization
Siqi Zhang and Niao He · 2018
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Adaptive stochastic natural gradient method for one-shot neural architecture search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Noshinari, Kento Uchida, Shota Saito, and Kouhei Nishida · 2019
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Five miracles of mirror descent, 2019
Sébastien Bubeck · 2019
DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E. Hinton · 2019
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XNAS: Neural architecture search with expert advice
Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, and Lihi Zelnik-Manor · 2019
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ASAP: Architecture search, anneal and prune
Asaf Noy, Niv Nayman, Tal Ridnik, Sivan Doveh, Itamar Friedman, Raja Giryes, and Lihi Zelnik-Manor · 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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SNAS: Stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
Later among the works it cites.
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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.
MANAS: Multi-agent neural architecture search
Fabio Maria Carlucci, Pedro M. Esperança, Marco Singh, Antoine Yang, Victor Gabillon, Xang Xu, Zewei Chen, and Jun Wang · 2019
Cited alongside, same era.
Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 2019
Cited alongside, same era.
AutoAugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 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.
sharpDARTS: Faster and more accurate differentiable architecture search
Andrew Hundt, Varun Jain, and Gregory D. Hager · 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
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Multinomial distribution learning for effective neural architecture search
Xiawu Zheng, Rongrong Ji, Lang Tang, Baochang Zhang, Jianzhuang Liu, and Qi Tian · 2019
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2019
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NAS-Bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Closest in time.
To share or not to share: A comprehensive appraisal of weight-sharing
Alo is Pourchot, Alexis Ducarouge, and Olivier Sigaud · 2020
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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 · 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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Efficient neural architecture search via proximal iterations
Quanming Yao, Ju Xu, Wei-Wei Tu, and Zhangxing Zhu · 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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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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