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Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs] (Dec. 2015). http:\/\/arxiv.org\/abs\/1512.03385 arXiv:1512.03385."},{"key":"e_1_3_2_1_12_1","volume-title":"Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580","author":"Hinton E","year":"2012","unstructured":"Geoffrey\u00a0 E Hinton , Nitish Srivastava , Alex Krizhevsky , Ilya Sutskever , and Ruslan\u00a0 R Salakhutdinov . 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 ( 2012 ). Geoffrey\u00a0E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan\u00a0R Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)."},{"key":"e_1_3_2_1_13_1","volume-title":"Laurens van\u00a0der Maaten, and Kilian\u00a0Q. 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Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_17_1","unstructured":"Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Y. LeCun Y. Bengio and P. Haffner. 1989. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation 1 4 (1989).  Y. LeCun Y. Bengio and P. Haffner. 1989. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation 1 4 (1989).","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_3_2_1_20_1","volume-title":"Network in network.arXiv preprint arXiv:1312.4400","author":"M. LIN, Q.","year":"2013","unstructured":"M. LIN, Q. CHEN, and S. YAN. 2013. Network in network.arXiv preprint arXiv:1312.4400 , 2013 . (2013). M. 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Hierarchical Representations for Efficient Architecture Search. arXiv:1711.00436 [cs, stat] (Feb. 2018). http:\/\/arxiv.org\/abs\/1711.00436 arXiv:1711.00436."},{"key":"e_1_3_2_1_22_1","volume-title":"DARTS: Differentiable Architecture Search. arXiv:1806.09055 [cs, stat] (April","author":"Liu Hanxiao","year":"2019","unstructured":"Hanxiao Liu , Karen Simonyan , and Yiming Yang . 2019 . DARTS: Differentiable Architecture Search. arXiv:1806.09055 [cs, stat] (April 2019). http:\/\/arxiv.org\/abs\/1806.09055 arXiv:1806.09055. Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. DARTS: Differentiable Architecture Search. arXiv:1806.09055 [cs, stat] (April 2019). http:\/\/arxiv.org\/abs\/1806.09055 arXiv:1806.09055."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3052758"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-7-125"},{"key":"e_1_3_2_1_26_1","unstructured":"Geoffrey Miller Peter Todd and Shailesh Hegde. 1989. Designing Neural Networks using Genetic Algorithms.Pages: 384.  Geoffrey Miller Peter Todd and Shailesh Hegde. 1989. Designing Neural Networks using Genetic Algorithms.Pages: 384."},{"key":"e_1_3_2_1_27_1","volume-title":"Large-Scale Evolution of Image Classifiers. arXiv:1703.01041 [cs] (June","author":"Real Esteban","year":"2017","unstructured":"Esteban Real , Sherry Moore , Andrew Selle , Saurabh Saxena , Yutaka\u00a0Leon Suematsu , Jie Tan , Quoc Le , and Alex Kurakin . 2017a. Large-Scale Evolution of Image Classifiers. arXiv:1703.01041 [cs] (June 2017 ). http:\/\/arxiv.org\/abs\/1703.01041 arXiv:1703.01041. Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka\u00a0Leon Suematsu, Jie Tan, Quoc Le, and Alex Kurakin. 2017a. Large-Scale Evolution of Image Classifiers. arXiv:1703.01041 [cs] (June 2017). http:\/\/arxiv.org\/abs\/1703.01041 arXiv:1703.01041."},{"key":"e_1_3_2_1_28_1","volume-title":"Large-Scale Evolution of Image Classifiers. (July","author":"Real Esteban","year":"2017","unstructured":"Esteban Real , Sherry Moore , Andrew Selle , Saurabh Saxena , Yutaka\u00a0Leon Suematsu , Jie Tan , Quoc\u00a0 V. Le , and Alexey Kurakin . 2017b. Large-Scale Evolution of Image Classifiers. (July 2017 ), 2902\u20132911. https:\/\/proceedings.mlr.press\/v70\/real17a.html ISSN : 2640-3498. Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka\u00a0Leon Suematsu, Jie Tan, Quoc\u00a0V. Le, and Alexey Kurakin. 2017b. Large-Scale Evolution of Image Classifiers. (July 2017), 2902\u20132911. https:\/\/proceedings.mlr.press\/v70\/real17a.html ISSN: 2640-3498."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Jeffrey\u00a0R Sampson. 1976. Adaptation in natural and artificial systems (John H. Holland).  Jeffrey\u00a0R Sampson. 1976. Adaptation in natural and artificial systems (John H. Holland).","DOI":"10.1137\/1018105"},{"key":"e_1_3_2_1_30_1","unstructured":"K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556 (2014).  K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_31_1","volume-title":"Practical Bayesian Optimization of Machine Learning Algorithms. 25","author":"Snoek Jasper","year":"2012","unstructured":"Jasper Snoek , Hugo Larochelle , and Ryan\u00a0 P Adams . 2012. Practical Bayesian Optimization of Machine Learning Algorithms. 25 ( 2012 ). https:\/\/proceedings.neurips.cc\/paper\/2012\/file\/05311655a15b75fab86956663e1819cd-Paper.pdf Jasper Snoek, Hugo Larochelle, and Ryan\u00a0P Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. 25 (2012). https:\/\/proceedings.neurips.cc\/paper\/2012\/file\/05311655a15b75fab86956663e1819cd-Paper.pdf"},{"key":"e_1_3_2_1_32_1","unstructured":"Rupesh\u00a0Kumar Srivastava Klaus Greff and J\u00fcrgen Schmidhuber. 2015. Highway Networks. (2015) 6.  Rupesh\u00a0Kumar Srivastava Klaus Greff and J\u00fcrgen Schmidhuber. 2015. Highway Networks. (2015) 6."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1162\/artl.2009.15.2.15202"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1162\/106365602320169811"},{"key":"e_1_3_2_1_35_1","unstructured":"F.\u00a0P. Such V. Madhavan E. Conti J. Lehman K.\u00a0O. Stanley and J. Clune. 2017. Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning.arXiv preprint arXiv:1712.06567 (2017).  F.\u00a0P. Such V. Madhavan E. Conti J. Lehman K.\u00a0O. Stanley and J. Clune. 2017. Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning.arXiv preprint arXiv:1712.06567 (2017)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Masanori Suganuma Shinichi Shirakawa and Tomoharu Nagao. 2017. A genetic programming approach to designing convolutional neural network architectures. (2017) 497\u2013504.  Masanori Suganuma Shinichi Shirakawa and Tomoharu Nagao. 2017. A genetic programming approach to designing convolutional neural network architectures. (2017) 497\u2013504.","DOI":"10.1145\/3071178.3071229"},{"key":"e_1_3_2_1_37_1","volume-title":"Evolving Deep Convolutional Neural Networks for Image Classification. arXiv:1710.10741 [cs] (March","author":"Sun Yanan","year":"2019","unstructured":"Yanan Sun , Bing Xue , Mengjie Zhang , and Gary\u00a0 G. Yen . 2019. Evolving Deep Convolutional Neural Networks for Image Classification. arXiv:1710.10741 [cs] (March 2019 ). http:\/\/arxiv.org\/abs\/1710.10741 arXiv:1710.10741. Yanan Sun, Bing Xue, Mengjie Zhang, and Gary\u00a0G. Yen. 2019. Evolving Deep Convolutional Neural Networks for Image Classification. arXiv:1710.10741 [cs] (March 2019). http:\/\/arxiv.org\/abs\/1710.10741 arXiv:1710.10741."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2983860"},{"key":"e_1_3_2_1_39_1","unstructured":"I. Sutskever J. Martens G. Dahl and G.\u00a0E. Hinton. 2013. Practical network blocks design with Q-learning.In International Conference of Machine Learning (ICML). (2013).  I. Sutskever J. Martens G. Dahl and G.\u00a0E. Hinton. 2013. Practical network blocks design with Q-learning.In International Conference of Machine Learning (ICML). (2013)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_41_1","unstructured":"Lingxi Xie and Alan Yuille. 2017. Genetic cnn. (2017) 1379\u20131388.  Lingxi Xie and Alan Yuille. 2017. Genetic cnn. (2017) 1379\u20131388."},{"key":"e_1_3_2_1_42_1","volume-title":"Aggregated Residual Transformations for Deep Neural Networks. arXiv:1611.05431 [cs] (April","author":"Xie Saining","year":"2017","unstructured":"Saining Xie , Ross Girshick , Piotr Doll\u00e1r , Zhuowen Tu , and Kaiming He. 2017. Aggregated Residual Transformations for Deep Neural Networks. arXiv:1611.05431 [cs] (April 2017 ). http:\/\/arxiv.org\/abs\/1611.05431 arXiv:1611.05431. Saining Xie, Ross Girshick, Piotr Doll\u00e1r, Zhuowen Tu, and Kaiming He. 2017. Aggregated Residual Transformations for Deep Neural Networks. arXiv:1611.05431 [cs] (April 2017). http:\/\/arxiv.org\/abs\/1611.05431 arXiv:1611.05431."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.05.037"},{"key":"e_1_3_2_1_44_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis . 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 ( 2016 ). Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1707.01083"},{"key":"e_1_3_2_1_46_1","volume-title":"Neural Architecture Search with Reinforcement Learning. arXiv:1611.01578 [cs] (Feb","author":"Zoph Barret","year":"2017","unstructured":"Barret Zoph and Quoc\u00a0 V. Le. 2017. 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