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Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation.
Designing neural networks using genetic algorithms
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A comparative analysis of selection schemes used in genetic algorithms
David E. Goldberg and Kalyanmoy Deb · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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An evolutionary algorithm that constructs recurrent neural networks
Peter J. Angeline, Gregory M. Saunders, and Jordan B. Pollack · 1994
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Evolving artificial neural networks
Xin Yao · 1999
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Evolving neural networks through augmenting topologies
Kenneth O Stanley and Risto Miikkulainen · 2002
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Neuroevolution: from architectures to learning
Dario Floreano, Peter Dürr, and Claudio Mattiussi · 2008
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A hypercube-based encoding for evolving large-scale neural networks
Kenneth O. Stanley, David B. D’Ambrosio, and Jason Gauci · 2009
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Algorithms for hyper-parameter optimization
James S. Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. Hoos, and K. Leyton-Brown · 2011
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Dan Yamins, and David D. Cox · 2013
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Raiders of the lost architecture: Kernels for bayesian optimization in conditional parameter spaces
Kevin Swersky, David Duvenaud, Jasper Snoek, Frank Hutter, and Michael Osborne · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Freeze-thaw bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T. Domhan, J. T. Springenberg, and F. Hutter · 2015
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Lstm: A search space odyssey
Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, and Jürgen Schmidhuber · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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An empirical exploration of recurrent network architectures
Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever · 2015
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Taking the human out of the loop: A review of bayesian optimization
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas · 2015
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Net2net: Accelerating learning via knowledge transfer
Tianqi Chen, Ian J. Goodfellow, and Jonathon Shlens · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Towards Automatically-Tuned Neural Networks
H. Mendoza, A. Klein, M. Feurer, J. Springenberg, and F. Hutter · 2016
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Convolutional neural fabrics
Shreyas Saxena and Jakob Verbeek · 2016
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Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
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Network morphism
Tao Wei, Changhu Wang, Yong Rui, and Chang Wen Chen · 2016
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Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2016
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SMASH: one-shot model architecture search through hypernetworks
Andrew Brock, Theodore Lim, James M. Ritchie, and Nick Weston · 2017
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A downsampled variant of imagenet as an alternative to the CIFAR datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Intriguing Properties of Adversarial Examples
Ekin D. Cubuk, Barret Zoph, Samuel S. Schoenholz, and Quoc V. Le · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance Devries and Graham W. Taylor · 2017
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Simple And Efficient Architecture Search for Convolutional Neural Networks
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2017
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Shake-shake regularization
Xavier Gastaldi · 2017
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Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2017
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Categorical reparameterization with gumbel-softmax
Auto-keras: Efficient neural architecture search with network morphism, 2018
Haifeng Jin, Qingquan Song, and Xia Hu · 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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Towards reproducible neural architecture and hyperparameter search
Aaron Klein, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2018
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Evolutionary Architecture Search For Deep Multitask Networks
Jason Liang, Elliot Meyerson, and Risto Miikkulainen · 2018
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Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing and Back
Elliot Meyerson and Risto Miikkulainen · 2018
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Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Hyperband: bandit-based configuration evaluation for hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat · 2017
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DeepArchitect: Automatically Designing and Training Deep Architectures
R. Negrinho and G. Gordon · 2017
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Fast neural architecture search of compact semantic segmentation models via auxiliary cells
Vladimir Nekrasov, Hao Chen, Chunhua Shen, and Ian D. Reid · 2018
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
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Dynamic Network Architectures
Prajit Ramachandran and Quoc V. Le · 2018
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From Nodes to Networks: Evolving Recurrent Neural Networks
Aditya Rawal and Risto Miikkulainen · 2018
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Differentiable neural network architecture search
Richard Shin, Charles Packer, and Dawn Song · 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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Transfer learning with neural automl
Catherine Wong, Neil Houlsby, Yifeng Lu, and Andrea Gesmundo · 2018
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Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
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Resource-efficient neural architect
Yanqi Zhou, Siavash Ebrahimi, Sercan Arık, 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
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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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Hyperparameter optimization
Matthias Feurer and Frank Hutter · 2019
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Automatic Machine Learning: Methods, Systems, Challenges
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, editors · 2019
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
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Aging Evolution for Image Classifier Architecture Search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V. Le · 2019
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Learning to design RNA
Frederic Runge, Danny Stoll, Stefan Falkner, and Frank Hutter · 2019
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Evaluating the search phase of neural architecture search
Christian Sciuto, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
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The evolved transformer
David R. So, Chen Liang, and Quoc V. Le · 2019
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Designing neural networks through neuroevolution
Kenneth Stanley, Jeff Clune, Joel Lehman, and Risto Miikkulainen · 2019
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Meta-learning
Joaquin Vanschoren · 2019
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SNAS: stochastic neural architecture search
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin · 2019
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Nas-bench-101: Towards reproducible neural architecture search
Chris Ying, Aaron Klein, Esteban Real, Eric Christiansen, Kevin Murphy, and Frank Hutter · 2019
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Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2019
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