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Neural architecture search (NAS) has gained increasing attention in the community of architecture design.
Estimates of the regression coefficient based on kendall’s tau
P. K. Sen · 1968
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Matrix analysis
R. A. Horn and C. R. Johnson · 2012
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Facenet: A Unified Embedding for Face Recognition and Clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Designing neural network architectures using reinforcement learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2017
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Deep pyramidal residual networks
D. Han, J. Kim, and J. Kim · 2017
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Overcoming catastrophic interference by conceptors
X. He and H. Jaeger · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2017
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Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song · 2017
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SGDR: stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Thinet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
G. Bender, P.-J. Kindermans, B. Zoph, V. Vasudevan, and Q. Le · 2018
Cited alongside, same era.
Efficient architecture search by network transformation
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang · 2018
Cited alongside, same era.
Double forward propagation for memorized batch normalization
Y. Guo, Q. Wu, C. Deng, J. Chen, and M. Tan · 2018
Cited alongside, same era.
Progressive neural architecture search
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy · 2018
Cited alongside, same era.
Neural architecture optimization
R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu · 2018
Cited alongside, same era.
Nas-fpn: Learning scalable feature pyramid architecture for object detection
G. Ghiasi, T.-Y. Lin, and Q. V. Le · 2019
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NAT: neural architecture transformer for accurate and compact architectures
Y. Guo, Y. Zheng, M. Tan, Q. Chen, J. Chen, P. Zhao, and J. Huang · 2019
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Random search and reproducibility for neural architecture search
L. Li and A. Talwalkar · 2019
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Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
C. Liu, L.-C. Chen, F. Schroff, H. Adam, W. Hua, A. L. Yuille, and L. Fei-Fei · 2019
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Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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Understanding and improving one-shot neural architecture optimization
R. Luo, T. Qin, and E. Chen · 2019
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Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
Cited alongside, same era.
Graph hypernetworks for neural architecture search
C. Zhang, M. Ren, and R. Urtasun · 2018
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2018
Cited alongside, same era.
Online adaptive asymmetric active learning for budgeted imbalanced data
Y. Zhang, P. Zhao, J. Cao, W. Ma, et al · 2018
Cited alongside, same era.
Adaptive cost-sensitive online classification
P. Zhao, Y. Zhang, M. Wu, S. C. Hoi, M. Tan, and J. Huang · 2018
Cited alongside, same era.
Discrimination-aware channel pruning for deep neural networks
Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J. Zhu · 2018
Cited alongside, same era.
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Xnas: Neural architecture search with expert advice
N. Nayman, A. Noy, T. Ridnik, I. Friedman, R. Jin, and L. Zelnik · 2019
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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Evaluating the search phase of neural architecture search
C. Sciuto, K. Yu, M. Jaggi, C. Musat, and M. Salzmann · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le · 2019
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, and K. Keutzer · 2019
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Snas: stochastic neural architecture search
S. Xie, H. Zheng, C. Liu, and L. Lin · 2019
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Continual learning of context-dependent processing in neural networks
G. Zeng, Y. Chen, B. Cui, and S. Yu · 2019
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From whole slide imaging to microscopy: Deep microscopy adaptation network for histopathology cancer image classification
Y. Zhang, H. Chen, Y. Wei, P. Zhao, J. Cao, et al · 2019
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Collaborative unsupervised domain adaptation for medical image diagnosis
Y. Zhang, Y. Wei, P. Zhao, S. Niu, et al · 2019
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Online adaptive asymmetric active learning with limited budgets
Y. Zhang, P. Zhao, S. Niu, Q. Wu, et al · 2019
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Bayesnas: A bayesian approach for neural architecture search
H. Zhou, M. Yang, J. Wang, and W. Pan · 2019
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Cost-sensitive portfolio selection via deep reinforcement learning
Y. Zhang, P. Zhao, Q. Wu, B. Li, et al · 2020
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