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Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models.
Applied Nonparametric Statistics
Wayne W. Daniel · 1990
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Optimal brain damage
Yann LeCun, John S. Denker, and Sara A. Solla · 1990
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Timit acoustic phonetic continuous speech corpus
John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, and Victor Zue · 1993
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G. Stork · 1993
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Fast Exact Multiplication by the Hessian
Barak A. Pearlmutter · 1993
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ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning Multiple Layers of Features from Tiny Images, 2009
Alex Krizhevsky · 2009
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Reading Digits in Natural Images with Unsupervised Feature Learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Perforatedcnns: Acceleration through elimination of redundant convolutions
Mikhail Figurnov, Aizhan Ibraimova, Dmitry P Vetrov, and Pushmeet Kohli · 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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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc V. Le · 2018
Cited alongside, same era.
Efficient architecture search by network transformation
Han Cai, Tianyao Chen, Weinan Zhang, Yong Yu, and Jun Wang · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing, 2018
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
Cited alongside, same era.
Faster gaze prediction with dense networks and fisher pruning
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
Xuanyi Dong and Yi Yang · 2020
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BRP-NAS: Prediction-based NAS using GCNs
Łukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah, Royson Lee, Hyeji Kim, and Nicholas D. Lane · 2020
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Nas-bench-nlp: Neural architecture search benchmark for natural language processing
Nikita Klyuchnikov, Ilya Trofimov, Ekaterina Artemova, Mikhail Salnikov, Maxim Fedorov, and Evgeny Burnaev · 2020
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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 · 2020
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Neural architecture search without training
Joseph Mellor, Jack Turner, Amos Storkey, and Elliot J. Crowley · 2020
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Lucas Theis, Iryna Korshunova, Alykhan Tejani, and Ferenc Huszár · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2019
Cited alongside, same era.
DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Cited alongside, same era.
Regularized Evolution for Image Classifier Architecture Search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
Cited alongside, same era.
Neural predictor for neural architecture search
Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, and Pieter-Jan Kindermans · 2019
Cited alongside, same era.
NAS-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, and Frank Hutter · 2019
Cited alongside, same era.
PyTorchCV Convolutional neural networks for computer vision, August 2020
Oleg Sémery · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, and Surya Ganguli · 2020
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Blockswap: Fisher-guided block substitution for network compression on a budget
Jack Turner, Elliot J. Crowley, Michael O’Boyle, Amos Storkey, and Gavin Gray · 2020
Later among the works it cites.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
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NPENAS: Neural predictor guided evolution for neural architecture search
Chen Wei, Chuang Niu, Yiping Tang, and Jimin Liang · 2020
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Econas: Finding proxies for economical neural architecture search
Dongzhan Zhou, Xinchi Zhou, Wenwei Zhang, Chen Change Loy, Shuai Yi, Xuesen Zhang, and Wanli Ouyang · 2020
Later among the works it cites.
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition
Abhinav Mehrotra, Alberto Gil Ramos, Sourav Bhattacharya, Łukasz Dudziak, Ravichander Vipperla, Thomas Chau, Mohamed S. Abdelfattah, Samin Ishtiaq, and Nicholas D. Lane · 2021
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