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2020 | OriginalPaper | Buchkapitel

Regularized Evolution for Macro Neural Architecture Search

verfasst von : George Kyriakides, Konstantinos Margaritis

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

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Abstract

Neural Architecture Search is becoming an increasingly popular research field and method to design deep learning architectures. Most research focuses on searching for small blocks of deep learning operations, or micro-search. This method yields satisfactory results but demands prior knowledge of the macro architecture’s structure. Generally, methods that do not utilize macro structure knowledge perform worse but are able to be applied to datasets of completely new domains. In this paper, we propose a macro NAS methodology which utilizes concepts of Regularized Evolution and Macro Neural Architecture Search (DeepNEAT), and apply it to the Fashion-MNIST dataset. By utilizing our method, we are able to produce networks that outperform other macro NAS methods on the dataset, when the same post-search inference methods are used. Furthermore, we are able to achieve 94.46% test accuracy, while requiring considerably less epochs to fully train our network.

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Vorheriges Kapitel Investigating the Problem of Cryptocurrency Price Prediction: A Deep Learning Approach

Nächstes Kapitel Using Multimodal Contextual Process Information for the Supervised Detection of Connector Lock Events

https://github.com/GeorgeKyriakides/nord/tree/AIAI2020/nord.

Assunçao, F., Lourenço, N., Machado, P., Ribeiro, B.: Denser: deep evolutionary network structured representation. Genet. Program Evolvable Mach. 20(1), 5–35 (2019)

Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Sequential deep learning for human action recognition. In: Salah, A.A., Lepri, B. (eds.) HBU 2011. LNCS, vol. 7065, pp. 29–39. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25446-8_4CrossRef

Byla, E., Pang, W.: DeepSwarm: optimising convolutional neural networks using swarm intelligence. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds.) UKCI 2019. AISC, vol. 1043, pp. 119–130. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-29933-0_10CrossRef

Cheng, Z., Yang, Q., Sheng, B.: Deep colorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 415–423 (2015)

Elsken, T., Metzen, J.H., Hutter, F.: Simple and efficient architecture search for convolutional neural networks. arXiv preprint arXiv:1711.04528 (2017)

He, K., Wang, Y., Hopcroft, J.: A powerful generative model using random weights for the deep image representation. In: Advances in Neural Information Processing Systems. pp. 631–639 (2016)

Jin, H., Song, Q., Hu, X.: Auto-keras: an efficient neural architecture search system. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1946–1956 (2019)

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Kyriakides, G., Margaritis, K.G.: Towards automated neural design: an open source, distributed neural architecture research framework. In: Proceedings of the 22nd Pan-Hellenic Conference on Informatics, pp. 113–116 (2018)

10.

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef

11.

Liu, C., et al.: Progressive neural architecture search. In: Proceedings of the European Conference on Computer Vision, ECCV, pp. 19–34 (2018)

12.

Liu, H., Simonyan, K., Vinyals, O., Fernando, C., Kavukcuoglu, K.: Hierarchical representations for efficient architecture search. arXiv preprint arXiv:1711.00436 (2017)

13.

Miikkulainen, R., et al.: Evolving deep neural networks. In: Artificial Intelligence in the Age of Neural Networks and Brain Computing, pp. 293–312. Elsevier (2019)

14.

Nayman, N., Noy, A., Ridnik, T., Friedman, I., Jin, R., Zelnik, L.: Xnas: neural architecture search with expert advice. In: Advances in Neural Information Processing Systems, pp. 1975–1985 (2019)

15.

Noy, A., et al.: Asap: Architecture search, anneal and prune. arXiv preprint arXiv:1904.04123 (2019)

16.

Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: Efficient neural architecture search via parameter sharing. arXiv preprint arXiv:1802.03268 (2018)

17.

Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4780–4789 (2019)

18.

Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)CrossRef

19.

Wu, B., et al.: Fbnet: hardware-aware efficient convnet design via differentiable neural architecture search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10734–10742 (2019)

20.

Wu, B., Iandola, F., Jin, P.H., Keutzer, K.: Squeezedet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 129–137 (2017)

21.

Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)

22.

Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)

23.

Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697–8710 (2018)

Titel: Regularized Evolution for Macro Neural Architecture Search
verfasst von: George Kyriakides
Konstantinos Margaritis
Verlag: Springer International Publishing
Buch: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-030-49185-7

Electronic ISBN: 978-3-030-49186-4

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-49186-4_10

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