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Networks found with Neural Architecture Search (NAS) achieve state-of-the-art performance in a variety of tasks, out-performing human-designed networks.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George E. Dahl, and Geoffrey E. Hinton · 2013
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Convolutional neural fabrics
Shreyas Saxena and Jakob Verbeek · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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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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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 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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Fractalnet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V Le, and Alexey Kurakin · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 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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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
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SMASH: One-shot model architecture search through hypernetworks
Andrew Brock, Theo Lim, J.M. Ritchie, and Nick Weston · 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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BOHB: robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Macro neural architecture search revisited
Hanzhang Hu, John Langford, Rich Caruana, Eric Horvitz, and Debadeepta Dey · 2018
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Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, and Eric P. Xing · 2018
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Hierarchical Representations for Efficient Architecture Search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 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
Cited alongside, same era.
Efficient progressive neural architecture search
Juan-Manuel Perez-Rua, Moez Baccouche, and Stéphane Pateux · 2018
Cited alongside, same era.
Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 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.
Learning time/memory-efficient deep architectures with budgeted super networks
Tom Veniat and Ludovic Denoyer · 2018
Cited alongside, same era.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
CATCH: context-based meta reinforcement learning for transferrable architecture search
Xin Chen, Yawen Duan, Zewei Chen, Hang Xu, Zihao Chen, Xiaodan Liang, Tong Zhang, and Zhenguo Li · 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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Adversarialnas: Adversarial neural architecture search for gans
Chen Gao, Yunpeng Chen, Si Liu, Zhenxiong Tan, and Shuicheng Yan · 2020
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A survey of the recent architectures of deep convolutional neural networks
Asifullah Khan, Anabia Sohail, Umme Zahoora, and Aqsa Saeed Qureshi · 2020
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Gp-nas: Gaussian process based neural architecture search
Zhihang Li, Teng Xi, Jiankang Deng, Gang Zhang, Shengzhao Wen, and Ran He · 2020
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Are labels necessary for neural architecture search?
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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.
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
Han Cai, Ligeng Zhu, and Song Han · 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.
Detnas: Backbone search for object detection, 2019
Yukang Chen, Tong Yang, Xiangyu Zhang, Gaofeng Meng, Xinyu Xiao, and Jian Sun · 2019
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
Cited alongside, same era.
Chenxi Liu, Piotr Dollár, Kaiming He, Ross B. Girshick, Alan L. Yuille, and Saining Xie · 2020
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Bridging the gap between sample-based and one-shot neural architecture search with BONAS
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, and Tong Zhang · 2020
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Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2020
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Pc-darts: Partial channel connections for memory-efficient architecture search
Yuhui Xu, Lingxi Xie, Xiaopeng Zhang, Xin Chen, Guo-Jun Qi, Qi Tian, and Hongkai Xiong · 2020
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Nas evaluation is frustratingly hard
Antoine Yang, Pedro M. Esperança, and Fabio M. Carlucci · 2020
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Neural Ensemble Search for Performant and Calibrated Predictions
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, and Yee Whye Teh · 2020
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Understanding and Robustifying Differentiable Architecture Search
Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, and Frank Hutter · 2020
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https://pytorch.org/vision/0.8/models.html
Torchvision · 2021
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Neural architecture search on imagenet in four gpu hours: A theoretically inspired perspective
Wuyang Chen, Xinyu Gong, and Zhangyang Wang · 2021
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Transnas-bench-101: Improving transferability and generalizability of cross-task neural architecture search
Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, and Zhenguo Li · 2021
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A survey on evolutionary neural architecture search
Yuqiao Liu, Yanan Sun, Bing Xue, Mengjie Zhang, Gary G Yen, and Kay Chen Tan · 2021
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EPE-NAS: efficient performance estimation without training for neural architecture search
Vasco Lopes, Saeid Alirezazadeh, and Luís A. Alexandre · 2021
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Guided evolution for neural architecture search
Vasco Lopes, Miguel Santos, Bruno Degardin, and Luís A Alexandre · 2021
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Neural architecture search without training
Joe Mellor, Jack Turner, Amos J. Storkey, and Elliot J. Crowley · 2021
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A comprehensive survey of neural architecture search: Challenges and solutions
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Poyao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang · 2021
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BANANAS: bayesian optimization with neural architectures for neural architecture search
Colin White, Willie Neiswanger, and Yash Savani · 2021
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How powerful are performance predictors in neural architecture search?
Colin White, Arber Zela, Binxin Ru, Yang Liu, and Frank Hutter · 2021
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Stronger NAS with weaker predictors
Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen, and Lu Yuan · 2021
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Benchenas: A benchmarking platform for evolutionary neural architecture search
Xiangning Xie, Yuqiao Liu, Yanan Sun, Gary G Yen, Bing Xue, and Mengjie Zhang · 2021
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Neural ensemble search for uncertainty estimation and dataset shift
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris C. Holmes, Frank Hutter, and Yee Whye Teh · 2021
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Arch-graph: Acyclic architecture relation predictor for task-transferable neural architecture search
Minbin Huang, Zhijian Huang, Changlin Li, Xin Chen, Hang Xu, Zhenguo Li, and Xiaodan Liang · 2022
Closest in time.
Efficient guided evolution for neural architecture search
Vasco Lopes, Miguel Santos, Bruno Degardin, and Luís A. Alexandre · 2022
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On redundancy and diversity in cell-based neural architecture search
Xingchen Wan, Binxin Ru, Pedro M Esperança, and Zhenguo Li · 2022
Closest in time.