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Differentiable neural architecture search (DARTS) is a popular method for neural architecture search (NAS), which performs cell-search and utilizes continuous relaxation to improve the search efficiency via gradient-based optimization.
Stabilizing DARTS with amended gradient estimation on architectural parameters
Kaifeng Bi, Changping Hu, Lingxi Xie, Xin Chen, Longhui Wei, and Qi Tian · 1910
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Matrix Analysis and Applied Linear Algebra
Carl Dean Meyer · 2000
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Evolving neural network through augmenting topologies
Kenneth O. Stanley and Risto Miikkulainen · 2002
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Second-order neural network training using complex-step directional derivative
Siyuan Shen, Tianjia Shao, Kun Zhou, Chenfanfu Jiang, Feng Luo, and Yin Yang · 2009
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Convex Optimization
Stephen P. Boyd and Lieven Vandenberghe · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2017
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On the properties of the softmax function with application in game theory and reinforcement learning
Bolin Gao and Lacra Pavel · 2017
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Highway and residual networks learn unrolled iterative estimation
Klaus Greff, Rupesh Kumar Srivastava, and Jürgen Schmidhuber · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, and Alexey Kurakin · 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 V. Le · 2018
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SMASH: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M. Ritchie, and Nick Weston · 2018
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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
Cited alongside, same era.
Residual connections encourage iterative inference
Stanislaw Jastrzebski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, and Yoshua Bengio · 2018
Cited alongside, same era.
Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan L. Yuille, Jonathan Huang, and Kevin Murphy · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 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.
Adapting neural architectures between domains
Yanxi Li, Zhaohui Yang, Yunhe Wang, and Chang Xu · 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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Understanding and robustifying differentiable architecture search
Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, and Frank Hutter · 2020
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Theory-inspired path-regularized differential network architecture search
Pan Zhou, Caiming Xiong, Richard Socher, and Steven Chu-Hong Hoi · 2020
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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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DARTS-: robustly stepping out of performance collapse without indicators
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Xuanyi Dong and Yi Yang · 2019
Cited alongside, same era.
Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
Cited alongside, same era.
DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Cited alongside, same era.
SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
Cited alongside, same era.
Stabilizing differentiable architecture search via perturbation-based regularization
Xiangning Chen and Cho-Jui Hsieh · 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.
Single path one-shot neural architecture search with uniform sampling
Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun · 2020
Cited alongside, same era.
Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun Lu, Xiaolin Wei, and Junchi Yan · 2021
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DOTS: decoupling operation and topology in differentiable architecture search
Yuchao Gu, Lijuan Wang, Yun Liu, Yi Yang, Yu-Huan Wu, Shao-Ping Lu, and Ming-Ming Cheng · 2021
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Mixsearch: Searching for domain generalized medical image segmentation architectures
Luyan Liu, Zhiwei Wen, Songwei Liu, Hong-Yu Zhou, Hongwei Zhu, Weicheng Xie, Linlin Shen, Kai Ma, and Yefeng Zheng · 2021
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A comprehensive survey of neural architecture search: Challenges and solutions
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Poyao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang · 2021
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Rethinking architecture selection in differentiable NAS
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, and Cho-Jui Hsieh · 2021
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IDARTS: interactive differentiable architecture search
Song Xue, Runqi Wang, Baochang Zhang, Tian Wang, Guodong Guo, and David S. Doermann · 2021
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idarts: Differentiable architecture search with stochastic implicit gradients
Miao Zhang, Steven W. Su, Shirui Pan, Xiaojun Chang, M. Ehsan Abbasnejad, and Reza Haffari · 2021
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β \beta -darts: Beta-decay regularization for differentiable architecture search
Peng Ye, Baopu Li, Yikang Li, Tao Chen, Jiayuan Fan, and Wanli Ouyang · 2022
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