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Neural Architecture Search (NAS) refers to automatically design the architecture.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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A* sampling
Chris J Maddison, Daniel Tarlow, and Tom Minka · 2014
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Fast r-cnn
Ross Girshick · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael Wilber, and Serge Belongie · 2016
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 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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Nsga-net: A multi-objective genetic algorithm for neural architecture search
Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh D. Dhebar, Kalyanmoy Deb, Erik D. Goodman, and Wolfgang Banzhaf · 2018
Earlier work this paper cites.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 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.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark B. Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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 · 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.
Proxylessnas: Direct neural architecture search on target task and hardware
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Later among the works it cites.
Auto-reid: Searching for a part-aware convnet for person re-identification
Ruijie Quan, Xuanyi Dong, Yu Wu, Linchao Zhu, and Yi Yang · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Later among the works it cites.
Meta architecture search
Albert Shaw, Wei Wei, Weiyang Liu, Le Song, and Bo Dai · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
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Mixconv: Mixed depthwise convolutional kernels
Mingxing Tan and Quoc V. Le · 2019
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Han Cai, Ligeng Zhu, and Song Han · 2019
Cited alongside, same era.
Data: Differentiable architecture approximation
Jianlong Chang, xinbang zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, and Chunhong Pan · 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.
Fast and practical neural architecture search
Jiequan Cui, Pengguang Chen, Ruiyu Li, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2019
Cited alongside, same era.
Global sparse momentum sgd for pruning very deep neural networks
Xiaohan Ding, guiguang ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, and Ji Liu · 2019
Cited alongside, same era.
One-shot neural architecture search via self-evaluated template network
Xuanyi Dong and Yi Yang · 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
Later among the works it cites.
Snas: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
Later among the works it cites.
Resource constrained neural network architecture search
Yunyang Xiong, Ronak Mehta, and Vikas Singh · 2019
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Evaluating the search phase of neural architecture search
Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
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Are all layers created equal
Chiyuan Zhang, Samy Bengio, and Yoram Singer · 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
Later among the works it cites.
Once for all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2020
Closest in time.
Nas-bench-102: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Closest in time.
Nas evaluation is frustratingly hard
Antoine Yang, Pedro M Esperança, and Fabio M Carlucci · 2020
Closest in time.
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
Closest in time.
Understanding and robustifying differentiable architecture search
Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, and Frank Hutter · 2020
Closest in time.