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Neural Architecture Search (NAS), aiming at automatically designing network architectures by machines, is hoped and expected to bring about a new revolution in machine learning.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 1911
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Segregation of pathways leading from area v2 to areas v4 and v5 of macaque monkey visual cortex
Stewart Shipp and Semir Zeki · 1985
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
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Knowledge projection for deep neural networks
Zhi Zhang, Guanghan Ning, and Zhihai He · 2017
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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 M Ritchie, and Nick Weston · 2018
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Deeparchitect: Automatically designing and training deep architectures
Renato Negrinho and Geoff Gordon · 2018
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Learning deep representations with probabilistic knowledge transfer
Nikolaos Passalis and Anastasios Tefas · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
Cited alongside, same era.
Progressive blockwise knowledge distillation for neural network acceleration
Hui Wang, Hanbin Zhao, Xi Li, and Xu Tan · 2018
Cited alongside, same era.
Practical block-wise neural network architecture generation
Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, and Cheng-Lin Liu · 2018
Cited alongside, same era.
Adaptive stochastic natural gradient method for one-shot neural architecture search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, and Kouhei Nishida · 2019
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
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 · 2019
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Searching for mobilenetv3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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Squeeze-and-excitation networks
J Hu, L Shen, S Albanie, G Sun, and E Wu · 2019
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Improving one-shot nas by suppressing the posterior fading
Xiang Li, Chen Lin, Chuming Li, Ming Sun, Wei Wu, Junjie Yan, and Wanli Ouyang · 2019
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Closest in time.
Mnasnet: Platform-aware neural architecture search for mobile
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Han Cai, Ligeng Zhu, and Song Han · 2019
Cited alongside, same era.
Renas: Reinforced evolutionary neural architecture search
Yukang Chen, Gaofeng Meng, Qian Zhang, Shiming Xiang, Chang Huang, Lisen Mu, and Xinggang Wang · 2019
Cited alongside, same era.
Scarletnas: Bridging the gap between scalability and fairness in neural architecture search
Xiangxiang Chu, Bo Zhang, Jixiang Li, Qingyuan Li, and Ruijun Xu · 2019
Cited alongside, same era.
Moga: Searching beyond mobilenetv3
Xiangxiang Chu, Bo Zhang, and Ruijun Xu · 2019
Cited alongside, same era.
Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search
Xiangxiang Chu, Bo Zhang, Ruijun Xu, and Jixiang Li · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 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.
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
Closest in time.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
Closest in time.
Mixconv: Mixed depthwise convolutional kernels
Mingxing Tan and Quoc V Le · 2019
Closest in time.
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
Closest in time.
Computation reallocation for object detection
Feng Liang, Chen Lin, Ronghao Guo, Ming Sun, Wei Wu, Junjie Yan, and Wanli Ouyang · 2020
Closest in time.
Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2020
Closest in time.
Nas evaluation is frustratingly hard
Antoine Yang, Pedro M Esperança, and Fabio M Carlucci · 2020
Closest in time.