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Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture.
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Deep residual learning for image recognition
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Densely connected convolutional networks
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Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 2017
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Large-scale evolution of image classifiers
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Aggregated residual transformations for deep neural networks
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Understanding and simplifying one-shot architecture search
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Proxylessnas: Direct neural architecture search on target task and hardware
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Atomnas: Fine-grained end-to-end neural architecture search
Jieru Mei, Yingwei Li, Xiaochen Lian, Xiaojie Jin, Linjie Yang, Alan Yuille, and Jianchao Yang · 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 · 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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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
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Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Netadapt: Platform-aware neural network adaptation for mobile applications
Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, and Hartwig Adam · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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Probabilistic neural architecture search
Francesco Paolo Casale, Jonathan Gordon, and Nicolo Fusi · 2019
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Data: Differentiable architecture approximation
Jianlong Chang, Yiwen Guo, GAOFENG MENG, SHIMING XIANG, Chunhong Pan, et al · 2019
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Autoaugment: Learning augmentation strategies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Mingxing Tan and Quoc V Le · 2019
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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
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Universally slimmable networks and improved training techniques
Jiahui Yu and Thomas S Huang · 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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Spinenet: Learning scale-permuted backbone for recognition and localization
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V Le, and Xiaodan Song · 2020
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Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions
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Bignas: Scaling up neural architecture search with big single-stage models
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Resnest: Split-attention networks
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R Manmatha, et al · 2020
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Rethinking bottleneck structure for efficient mobile network design
Daquan Zhou, Qibin Hou, Yunpeng Chen, Jiashi Feng, and Shuicheng Yan · 2020
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