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The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search.
Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L. Yuille, and Li Fei-Fei · 1901
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Probabilistic neural architecture search
Francesco Paolo Casale, Jonathan Gordon, and Nicolo Fusi · 1902
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Fast task-aware architecture inference
Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, and Jesse Berent · 1902
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 1902
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Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 1902
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Alphax: exploring neural architectures with deep neural networks and monte carlo tree search
Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, and Rodrigo Fonseca · 1903
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Inductive transfer for neural architecture optimization
Martin Wistuba and Tejaswini Pedapati · 1903
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Graphnas: Graph neural architecture search with reinforcement learning
Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu · 1904
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Exploring randomly wired neural networks for image recognition
Saining Xie, Alexander Kirillov, Ross B. Girshick, and Kaiming He · 1904
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Computational graphs and rounding error
Friedrich L Bauer · 1974
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The application of bayesian methods for seeking the extremum
Jonas Mockus, Vytautas Tiešis, and Antanas Žilinskas · 1978
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Learning from Delayed Rewards
Christopher John Cornish Hellaby Watkins · 1989
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Nonlinear programming: sequential unconstrained minimization techniques , volume 4
Anthony V Fiacco and Garth P McCormick · 1990
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A comparative analysis of selection schemes used in genetic algorithms
David E. Goldberg and Kalyanmoy Deb · 1990
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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On-line q-learning using connectionist systems
G. A. Rummery and M. Niranjan · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Bidirectional recurrent neural networks
Mike Schuster and Kuldip K. Paliwal · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Reinforcement learning - an introduction
Richard S. Sutton and Andrew G. Barto · 1998
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Meta-learning by landmarking various learning algorithms
Bernhard Pfahringer, Hilan Bensusan, and Christophe G. Giraud-Carrier · 2000
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New support vector algorithms
Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, and Peter L. Bartlett · 2000
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A taxonomy of global optimization methods based on response surfaces
Donald R. Jones · 2001
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Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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Evolution strategies - A comprehensive introduction
Hans-Georg Beyer and Hans-Paul Schwefel · 2002
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A fast and elitist multiobjective genetic algorithm: NSGA-II
Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T. Meyarivan · 2002
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Evolutionary computation - a unified approach
Kenneth A. De Jong · 2006
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Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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Gaussian processes for machine learning
Carl Edward Rasmussen and Christopher K. I. Williams · 2006
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Optimal Transport: Old and New
Cédric Villani · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Multiple objective decision making—methods and applications: a state-of-the-art survey , volume 164
C-L Hwang and Abu Syed Md Masud · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
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Multi-objective optimization
Kalyanmoy Deb · 2014
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Automatic classifier selection for non-experts
Matthias Reif, Faisal Shafait, Markus Goldstein, Thomas M. Breuel, and Andreas Dengel · 2014
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A survey of decomposition methods for multi-objective optimization
Alejandro Santiago Pineda, Héctor Joaquín Fraire Huacuja, Bernabé Dorronsoro, Johnatan E. Pecero, Claudia Gómez Santillán, Juan Javier González Barbosa, and Jos’e Carlos Soto Monterrubio · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Cited alongside, same era.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
Tobias Domhan, Jost Tobias Springenberg, and Frank Hutter · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2015
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz · 2015
Cited alongside, same era.
Path-level network transformation for efficient architecture search
Han Cai, Jiacheng Yang, Weinan Zhang, Song Han, and Yong Yu · 2018
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Intriguing properties of adversarial examples
Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, and Quoc V. Le · 2018
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Dpp-net: Device-aware progressive search for pareto-optimal neural architectures
Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, and Min Sun · 2018
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Simple and efficient architecture search for convolutional neural networks
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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AMC: automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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Speeding up the hyperparameter optimization of deep convolutional neural networks
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Tianqi Chen, Ian J. Goodfellow, and Jonathon Shlens · 2016
Cited alongside, same era.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Cited alongside, same era.
Selecting near-optimal learners via incremental data allocation
Ashish Sabharwal, Horst Samulowitz, and Gerald Tesauro · 2016
Cited alongside, same era.
Convolutional neural fabrics
Shreyas Saxena and Jakob Verbeek · 2016
Cited alongside, same era.
Neural networks designing neural networks: multi-objective hyper-parameter optimization
Sean C. Smithson, Guang Yang, Warren J. Gross, and Brett H. Meyer · 2016
Cited alongside, same era.
Deep kernel learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing · 2016
Cited alongside, same era.
Tobias Hinz, Nicolás Navarro-Guerrero, Sven Magg, and Stefan Wermter · 2018
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MONAS: multi-objective neural architecture search using reinforcement learning
Chi-Hung Hsu, Shu-Huan Chang, Da-Cheng Juan, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, and Shih-Chieh Chang · 2018
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Macro neural architecture search revisited
Hanzhang Hu, John Langford, Rich Caruana, Eric Horvitz, and Debadeepta Dey · 2018
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Condensenet: An efficient densenet using learned group convolutions
Gao Huang, Shichen Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2018
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Neural architecture search with bayesian optimisation and optimal transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, and Eric P. Xing · 2018
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Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan L. Yuille, Jonathan Huang, and Kevin Murphy · 2018
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Hierarchical representations for efficient architecture search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu · 2018
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NSGA-NET: A multi-objective genetic algorithm for neural architecture search
Zhichao Lu, Ian Whalen, Vishnu Boddeti, Yashesh D. Dhebar, Kalyanmoy Deb, Erik D. Goodman, and Wolfgang Banzhaf · 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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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V. Le · 2018
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Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search
Masanori Suganuma, Mete Ozay, and Takayuki Okatani · 2018
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Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V. Le · 2018
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Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations
Martin Wistuba · 2018
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Practical deep learning architecture optimization
Martin Wistuba · 2018
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Transfer learning with neural automl
Catherine Wong, Neil Houlsby, Yifeng Lu, and Andrea Gesmundo · 2018
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Yoshihiro Yamada, Masakazu Iwamura, and Koichi Kise · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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Practical block-wise neural network architecture generation
Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, and Cheng-Lin Liu · 2018
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Resource-efficient neural architect
Yanqi Zhou, Siavash Ebrahimi, Sercan Ömer Arik, Haonan Yu, Hairong Liu, and Greg Diamos · 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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ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
Closest in time.
Learnable embedding space for efficient neural architecture compression
Shengcao Cao, Xiaofang Wang, and Kris M. Kitani · 2019
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Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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TAPAS: train-less accuracy predictor for architecture search
Roxana Istrate, Florian Scheidegger, Giovanni Mariani, Dimitrios S. Nikolopoulos, Costas Bekas, and A. Cristiano I. Malossi · 2019
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DARTS: differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
Closest in time.
Aging evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
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Evolutionary search for adversarially robust neural networks
Mathieu Sinn, Martin Wistuba, Beat Buesser, Maria-Irina Nicolae, and Minh Tran · 2019
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The evolved transformer
David So, Quoc Le, and Chen Liang · 2019
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Nas-unet: Neural architecture search for medical image segmentation
Yu Weng, Tianbao Zhou, Yujie Li, and Xiaoyu Qiu · 2019
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SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Transferable automl by model sharing over grouped datasets
Chao Xue, Junchi Yan, Rong Yan, Stephen M. Chu, Yonggang Hu, and Yonghua Lin · 2019
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Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2019
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