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Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML).
EAT-NAS: Elastic architecture transfer for accelerating large-scale neural architecture search
Jiemin Fang, Yukang Chen, Xinbang Zhang, Qian Zhang, Chang Huang, Gaofeng Meng, Wenyu Liu, and Xinggang Wang · 1901
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Task2Vec: Task embedding for meta-learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless C. Fowlkes, Stefano Soatto, and Pietro Perona · 1902
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sharpdarts: Faster and more accurate differentiable architecture search
Andrew Hundt, Varun Jain, and Gregory D. Hager · 1903
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 1903
Earlier work this paper cites.
Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Xin Chen, Lingxi Xie, Jun Wu, and Qi Tian · 1904
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DARTS+: improved differentiable architecture search with early stopping
Hanwen Liang, Shifeng Zhang, Jiacheng Sun, Xingqiu He, Weiran Huang, Kechen Zhuang, and Zhenguo Li · 1909
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Understanding and robustifying differentiable architecture search
Arber Zela, Thomas Elsken, Tonmoy Saikia, Yassine Marrakchi, Thomas Brox, and Frank Hutter · 1909
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Fair DARTS: eliminating unfair advantages in differentiable architecture search
Xiangxiang Chu, Tianbao Zhou, Bo Zhang, and Jixiang Li · 1911
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Meta-learning of neural architectures for few-shot learning
Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, and Frank Hutter · 1911
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Designing neural networks using genetic algorithms
Geoffrey F. Miller, Peter M. Todd, and Shailesh U. Hegde · 1989
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Methods of Information Geometry , volume 191
Shun-ichi Amari and Hiroshi Nagaoka · 2000
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Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results
Pavel B. Brazdil, Carlos Soares, and Joaquim Pinto da Costa · 2003
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To transfer or not to transfer
Michael T. Rosenstein, Zvika Marx, Leslie Pack Kaelbling, and Thomas G. Dietterich · 2005
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An iterative process for building learning curves and predicting relative performance of classifiers
Rui Leite and Pavel Brazdil · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
Cited alongside, same era.
Optimal transport - Old and new , volume 338, pages xxii+973
Cédric Villani · 2012
Cited alongside, same era.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
Cited alongside, same era.
Metalearning: a survey of trends and technologies
Christiane Lemke, Marcin Budka, and Bogdan Gabrys · 2013
Cited alongside, same era.
Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew B. Blaschko, and Andrea Vedaldi · 2013
Cited alongside, same era.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
Practical network blocks design with q-learning
Zhao Zhong, Junjie Yan, and Cheng-Lin Liu · 2017
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2017
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Understanding and simplifying one-shot architecture search
Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le · 2018
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Proxylessnas: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2018
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Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution
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Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2016
Cited alongside, same era.
Similarity mapping with enhanced siamese network for multi-object tracking
Minyoung Kim, Stefano Alletto, and Luca Rigazio · 2016
Cited alongside, same era.
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2018
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Frederic Runge, Danny Stoll, Stefan Falkner, and Frank Hutter · 2018
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Joaquin Vanschoren · 2018
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Transfer automatic machine learning
Catherine Wong, Neil Houlsby, Yifeng Lu, and Andrea Gesmundo · 2018
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Taskonomy: Disentangling task transfer learning
Amir Roshan Zamir, Alexander Sax, William B. Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Towards warm-starting darts
Maurice Houben · 2019
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Geometric dataset distances via optimal transport
David Alvarez-Melis and Nicolo Fusi · 2020
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Stabilizing differentiable architecture search via perturbation-based regularization
Xiangning Chen and Cho-Jui Hsieh · 2020
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Towards fast adaptation of neural architectures with meta learning
Dongze Lian, Yin Zheng, Yintao Xu, Yanxiong Lu, Leyu Lin, Peilin Zhao, Junzhou Huang, and Shenghua Gao · 2020
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A comprehensive survey of neural architecture search: Challenges and solutions, 2021
Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Xiaojiang Chen, and Xin Wang · 2021
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