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Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the efficacy of zero-cost proxies in various NAS benchmarks.
Hawq-v2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 1911
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Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael C Mozer and Paul Smolensky · 1988
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Probabilistic backpropagation for scalable learning of bayesian neural networks
José Miguel Hernández-Lobato and Ryan Adams · 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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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric P Xing · 2018
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Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu · 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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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 2019
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One-shot neural architecture search via self-evaluated template network
Xuanyi Dong and Yezhou Yang · 2019
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Single path one-shot neural architecture search with uniform sampling
Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun · 2019
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2019
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DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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On network design spaces for visual recognition
Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, and Piotr Dollár · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 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.
Once for all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 2020
Cited alongside, same era.
Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Cited alongside, same era.
A generic graph-based neural architecture encoding scheme for predictor-based nas
Tnasp: A transformer-based nas predictor with a self-evolution framework
Shun Lu, Jixiang Li, Jianchao Tan, Sen Yang, and Ji Liu · 2021
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Ompq: Orthogonal mixed precision quantization
Yuexiao Ma, Taisong Jin, Xiawu Zheng, Yan Wang, Huixia Li, Guannan Jiang, Wei Zhang, and Rongrong Ji · 2021
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Neural architecture search without training
Joseph Mellor, Jack Turner, Amos Storkey, and Elliot J. Crowley · 2021
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Evaluating efficient performance estimators of neural architectures
Xuefei Ning, Changcheng Tang, Wenshuo Li, Zixuan Zhou, Shuang Liang, Huazhong Yang, and Yu Wang · 2021
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Vitas: Vision transformer architecture search
Xiu Su, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu · 2021
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Xuefei Ning, Yin Zheng, Tianchen Zhao, Yu Wang, and Huazhong Yang · 2020
Cited alongside, same era.
Automl-zero: Evolving machine learning algorithms from scratch
Esteban Real, Chen Liang, David So, and Quoc Le · 2020
Cited alongside, same era.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli · 2020
Cited alongside, same era.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herv’e J’egou · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Cited alongside, same era.
Neural predictor for neural architecture search
Wei Wen, Hanxiao Liu, Yiran Chen, Hai Li, Gabriel Bender, and Pieter-Jan Kindermans · 2020
Cited alongside, same era.
Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao · 2021
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EZNAS: Evolving zero-cost proxies for neural architecture scoring
Yash Akhauri, Juan Pablo Munoz, Nilesh Jain, and Ravishankar Iyer · 2022
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Entropy-driven mixed-precision quantization for deep network design on iot devices
Zhenhong Sun, Ce Ge, Junyan Wang, Ming Lin, Hesen Chen, Hao Li, and Xiuyu Sun · 2022
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Npenas: Neural predictor guided evolution for neural architecture search
Chen Wei, Chuang Niu, Yiping Tang, Yue Wang, Haihong Hu, and Jimin Liang · 2022
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Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks
Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, and Frank Hutter · 2022
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Gradsign: Model performance inference with theoretical insights
Zhihao Zhang and Zhihao Jia · 2022
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Training-free transformer architecture search
Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, and Rongrong Ji · 2022
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Freerea: Training-free evolution-based architecture search
Niccolò Cavagnero, Luca Robbiano, Barbara Caputo, and Giuseppe Averta · 2023
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Zico: Zero-shot NAS via inverse coefficient of variation on gradients
Guihong Li, Yuedong Yang, Kartikeya Bhardwaj, and Radu Marculescu · 2023
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Pinat: A permutation invariance augmented transformer for nas predictor
Shun Lu, Yu Hu, Peihao Wang, Yan Han, Jianchao Tan, Jixiang Li, Sen Yang, and Ji Liu · 2023
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Prenas: Preferred one-shot learning towards efficient neural architecture search
Haibin Wang, Ce Ge, Hesen Chen, and Xiuyu Sun · 2023
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Sigeo: Sub-one-shot NAS via information theory and geometry of loss landscape
Hua Zheng, Kuang-Hung Liu, Igor Fedorov, Xin Zhang, Wen-Yen Chen, and Wei Wen · 2023
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