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Komodakis, \"Object detection via a multi-region and semantic segmentation-aware CNN model,\" in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1134--1142.","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"e_1_3_2_1_6_1","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy C.","year":"2015","unstructured":"C. Szegedy , W. Liu , Y. Jia , P. Sermanet , S. Reed , D. Anguelov , D. Erhan , V. Vanhoucke , and A. Rabinovich , \" Going deeper with convolutions ,\" in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015 , pp. 1 -- 9 . C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. 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Raskar, \"Designing neural network architectures using reinforcement learning,\" arXiv preprint arXiv:1611.02167, 2016."},{"key":"e_1_3_2_1_22_1","first-page":"2902","article-title":"Large-scale evolution of image classifiers","author":"Real E.","year":"2017","unstructured":"E. Real , S. Moore , A. Selle , S. Saxena , Y. L. Suematsu , J. Tan , Q. V. Le , and A. Kurakin , \" Large-scale evolution of image classifiers ,\" in Proceedings of the International Conference on Machine Learning , 2017 , pp. 2902 -- 2911 . E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin, \"Large-scale evolution of image classifiers,\" in Proceedings of the International Conference on Machine Learning, 2017, pp. 2902--2911.","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"e_1_3_2_1_23_1","first-page":"1379","article-title":"Genetic CNN","author":"Xie L.","year":"2017","unstructured":"L. Xie and A. Yuille , \" Genetic CNN ,\" in Proceedings of the IEEE International Conference on Computer Vision , 2017 , pp. 1379 -- 1388 . L. Xie and A. Yuille, \"Genetic CNN,\" in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 1379--1388.","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3321707.3321721"},{"key":"e_1_3_2_1_25_1","first-page":"4095","article-title":"Efficient neural architecture search via parameters sharing","author":"Pham H.","year":"2018","unstructured":"H. Pham , M. Guan , B. Zoph , Q. Le , and J. Dean , \" Efficient neural architecture search via parameters sharing ,\" in Proceedings of the International Conference on Machine Learning , 2018 , pp. 4095 -- 4104 . H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, \"Efficient neural architecture search via parameters sharing,\" in Proceedings of the International Conference on Machine Learning, 2018, pp. 4095--4104.","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2983860"},{"key":"e_1_3_2_1_27_1","first-page":"1","article-title":"Particle swarm optimisation for evolving deep neural networks for image classification by evolving and stacking transferable blocks","author":"Wang B.","year":"2020","unstructured":"B. Wang , B. Xue , and M. Zhang , \" Particle swarm optimisation for evolving deep neural networks for image classification by evolving and stacking transferable blocks ,\" in Proceedings of the IEEE Congress on Evolutionary Computation , 2020 , pp. 1 -- 8 . B. Wang, B. Xue, and M. 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