Fetching the paper…
Reading the bibliography…
Neural Architecture Search (NAS) has been explosively studied to automate the discovery of top-performer neural networks.
Complexity of linear regions in deep networks
Boris Hanin and David Rolnick · 1901
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, Halbert White, et al · 1989
Earlier work this paper cites.
Bignas: Scaling up neural architecture search with big single-stage models
Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas Huang, Xiaodan Song, Ruoming Pang, and Quoc Le · 2003
Earlier work this paper cites.
How to train your super-net: An analysis of training heuristics in weight-sharing nas
Kaicheng Yu, Rene Ranftl, and Mathieu Salzmann · 2003
Earlier work this paper cites.
Fitting the search space of weight-sharing nas with graph convolutional networks
Xin Chen, Lingxi Xie, Jun Wu, Longhui Wei, Yuhui Xu, and Qi Tian · 2004
Earlier work this paper cites.
Hournas: Extremely fast neural architecture search through an hourglass lens
Zhaohui Yang, Yunhe Wang, Dacheng Tao, Xinghao Chen, Jianyuan Guo, Chunjing Xu, Chao Xu, and Chang Xu · 2005
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Deep neural networks with random gaussian weights: A universal classification strategy?
Raja Giryes, Guillermo Sapiro, and Alex M Bronstein · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
Earlier work this paper cites.
Smash: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2017
Earlier work this paper cites.
A downsampled variant of imagenet as an alternative to the cifar datasets, 2017
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
Earlier work this paper cites.
Deep pyramidal residual networks
Dongyoon Han, Jiwhan Kim, and Junmo Kim · 2017
Earlier work this paper cites.
Notes on the number of linear regions of deep neural networks
Guido Montúfar · 2017
Earlier work this paper cites.
On the expressive power of deep neural networks
Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
Earlier work this paper cites.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Earlier work this paper cites.
Mean field residual networks: On the edge of chaos
Ge Yang and Samuel Schoenholz · 2017
Earlier work this paper cites.
Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 2018
Earlier work this paper cites.
Proxylessnas: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2018
Cited alongside, same era.
Searching for efficient multi-scale architectures for dense image prediction
Liang-Chieh Chen, Maxwell Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, and Jon Shlens · 2018
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Cited alongside, same era.
Neural architecture optimization
Renqian Luo, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2018
Cited alongside, same era.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Later among the works it cites.
Understanding architectures learnt by cell-based neural architecture search
Yao Shu, Wei Wang, and Shaofeng Cai · 2019
Later among the works it cites.
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
Later among the works it cites.
Neural predictor for neural architecture search
Wei Wen, Hanxiao Liu, Hai Li, Yiran Chen, Gabriel Bender, and Pieter-Jan Kindermans · 2019
Later among the works it cites.
Disentangling trainability and generalization in deep learning
Lechao Xiao, Jeffrey Pennington, and Samuel S Schoenholz · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean · 2018
Cited alongside, same era.
Bounding and counting linear regions of deep neural networks
Thiago Serra, Christian Tjandraatmadja, and Srikumar Ramalingam · 2018
Cited alongside, same era.
Snas: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 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.
Initialization of relus for dynamical isometry
Rebekka Burkholz and Alina Dubatovka · 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.
On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
Cited alongside, same era.
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 · 2019
Later among the works it cites.
Greg Yang · 2019
Later among the works it cites.
Bayesnas: A bayesian approach for neural architecture search
Hongpeng Zhou, Minghao Yang, Jun Wang, and Wei Pan · 2019
Later among the works it cites.
Stabilizing differentiable architecture search via perturbation-based regularization
Xiangning Chen and Cho-Jui Hsieh · 2020
Later among the works it cites.
Nas-bench-102: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
Later among the works it cites.
Nats-bench: Benchmarking nas algorithms for architecture topology and size
Xuanyi Dong, Lu Liu, Katarzyna Musial, and Bogdan Gabrys · 2020
Later among the works it cites.
Autogan-distiller: Searching to compress generative adversarial networks
Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan Lin, and Zhangyang Wang · 2020
Later among the works it cites.
Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2020
Later among the works it cites.
Semi-supervised neural architecture search
Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, and Tie-Yan Liu · 2020
Later among the works it cites.
Neural architecture search without training
Joseph Mellor, Jack Turner, Amos Storkey, and Elliot J Crowley · 2020
Later among the works it cites.
Trainability of relu networks and data-dependent initialization
Yeonjong Shin and George Em Karniadakis · 2020
Later among the works it cites.
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
Later among the works it cites.
Efficientdet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V Le · 2020
Later among the works it cites.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Later among the works it cites.
On the number of linear regions of convolutional neural networks
Huan Xiong, Lei Huang, Mengyang Yu, Li Liu, Fan Zhu, and Ling Shao · 2020
Later among the works it cites.