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This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem.
Choice of basis for laplace approximation
David J. C. MacKay · 1998
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Bayesian Data Analysis
Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin · 2004
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Pattern Recognition and Machine Learning
Christopher Bishop · 2016
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Net2net: Accelerating learning via knowledge transfer
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Variational inference: A review for statisticians, 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Autoencoding variational inference for topic models
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Memory augmented neural networks with wormhole connections, 2017
Yoshua Bengio Caglar Gulcehre, Sarath Chandar · 2017
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A downsampled variant of imagenet as an alternative to the cifar datasets, 2017
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Latent dirichlet allocation
Michael I. Jordan David M. Blei, Andrew Y. Ng · 2017
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Deep pyramidal residual networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Hierarchical multi-scale attention networks for action recognition, 2017
Shiyang Yan, Jeremy S. Smith, Wenjin Lu, and Bailing Zhang · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 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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Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 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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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Pathwise derivatives beyond the reparameterization trick
Fritz Obermeyer Martin Jankowiak · 2018
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Efficient neural architecture search via parameters sharing
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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SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Bayesnas: A bayesian approach for neural architecture search
Hongpeng Zhou, Minghao Yang, Jun Wang, and Wei Pan · 2019
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Stabilizing differentiable architecture search via perturbation-based regularization
Xiangning Chen and Cho-Jui Hsieh · 2020
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Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
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Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 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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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2018
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ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
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Probabilistic neural architecture search, 2019
Francesco Paolo Casale, Jonathan Gordon, and Nicolo Fusi · 2019
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
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Searching for a robust neural architecture in four gpu hours
Xuanyi Dong and Yi Yang · 2019
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Dsnas: Direct neural architecture search without parameter retraining, 2020
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A neural dirichlet process mixture model for task-free continual learning,” in international conference on learning representations
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Geometry-aware gradient algorithms for neural architecture search, 2020
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Understanding architectures learnt by cell-based neural architecture search
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PC-DARTS: Partial channel connections for memory-efficient architecture search
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Nas evaluation is frustratingly hard
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Efficient neural interaction function search for collaborative filtering
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Overcoming multi-model forgetting in one-shot nas with diversity maximization
M. Zhang, H. Li, S. Pan, X. Chang, and S. Su · 2020
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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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