Fetching the paper…
Reading the bibliography…
Weight-sharing neural architecture search (NAS) is an effective technique for automating efficient neural architecture design.
Differential-geometrical methods in statistics
Shun-ichi Amari · 1985
Earlier work this paper cites.
Divergence measures and message passing
Tom Minka et al · 2005
Earlier work this paper cites.
Hat: Hardware-aware transformers for efficient natural language processing
Hanrui Wang, Zhanghao Wu, Zhijian Liu, Han Cai, Ligeng Zhu, Chuang Gan, and Song Han · 2005
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Attentivenas: Improving neural architecture search via attentive sampling
Dilin Wang, Meng Li, Chengyue Gong, and Vikas Chandra · 2011
Earlier work this paper cites.
Machine learning: a probabilistic perspective , chapter 21
Kevin P Murphy · 2012
Earlier work this paper cites.
Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
Earlier work this paper cites.
Collecting a large-scale dataset of fine-grained cars
Jonathan Krause, Jia Deng, Michael Stark, and Li Fei-Fei · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
Earlier work this paper cites.
Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Like what you like: Knowledge distill via neuron selectivity transfer
Zehao Huang and Naiyan Wang · 2017
Earlier work this paper cites.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
Earlier work this paper cites.
Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Mia Xu Chen, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, et al · 2018
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric Xing · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
Cited alongside, same era.
Learning deep representations with probabilistic knowledge transfer
Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Later among the works it cites.
Relational knowledge distillation
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Later among the works it cites.
Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 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.
Universally slimmable networks and improved training techniques
Jiahui Yu and Thomas S Huang · 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…
Nikolaos Passalis and Anastasios Tefas · 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.
Variational inference with tail-adaptive f-divergence
Dilin Wang, Hao Liu, and Qiang Liu · 2018
Cited alongside, same era.
Slimmable neural networks
Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas Huang · 2018
Cited alongside, same era.
Variational information distillation for knowledge transfer
Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D Lawrence, and Zhenwen Dai · 2019
Cited alongside, same era.
Overcoming multi-model forgetting
Yassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C Davison, Mathieu Salzmann, and Claudiu Musat · 2019
Cited alongside, same era.
Customizable architecture search for semantic segmentation
Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, and Tao Mei · 2019
Later among the works it cites.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2019
Later among the works it cites.
Fbnetv3: Joint architecture-recipe search using neural acquisition function
Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Bichen Wu, Zijian He, Zhen Wei, Kan Chen, Yuandong Tian, Matthew Yu, Peter Vajda, et al · 2020
Later among the works it cites.
Densely connected search space for more flexible neural architecture search
Jiemin Fang, Yuzhu Sun, Qian Zhang, Yuan Li, Wenyu Liu, and Xinggang Wang · 2020
Later among the works it cites.
Block-wisely supervised neural architecture search with knowledge distillation
Changlin Li, Jiefeng Peng, Liuchun Yuan, Guangrun Wang, Xiaodan Liang, Liang Lin, and Xiaojun Chang · 2020
Later among the works it cites.
Distilling optimal neural networks: Rapid search in diverse spaces
Bert Moons, Parham Noorzad, Andrii Skliar, Giovanni Mariani, Dushyant Mehta, Chris Lott, and Tijmen Blankevoort · 2020
Later among the works it cites.
Cream of the crop: Distilling prioritized paths for one-shot neural architecture search
Houwen Peng, Hao Du, Hongyuan Yu, QI LI, Jing Liao, and Jianlong Fu · 2020
Later among the works it cites.
Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
Later among the works it cites.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2020
Later among the works it 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 · 2020
Later among the works it cites.
Cm-nas: Rethinking cross-modality neural architectures for visible-infrared person re-identification
Chaoyou Fu, Yibo Hu, Xiang Wu, Hailin Shi, Tao Mei, and Ran He · 2021
Closest in time.