Neural Architecture Search using Particle Swarm and Ant Colony Optimization

J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-International Conference on Neural Networks , vol. 4. IEEE, 1995, pp. 1942–1948

1948

Earlier work this paper cites.

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

1956

Earlier work this paper cites.

M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on evolutionary computation , vol. 1, no. 1, pp. 53–66, 1997

1997

Earlier work this paper cites.

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998

1998

Earlier work this paper cites.

R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in International conference on evolutionary programming . Springer, 1998, pp. 611–616

1998

Earlier work this paper cites.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 , 2014

Original

2014

Earlier work this paper cites.

A. Krizhevsky, V. Nair, and G. Hinton, “The cifar-10 dataset,” online: http://www. cs. toronto. edu/kriz/cifar. html , vol. 55, 2014

2014

Earlier work this paper cites.

B. A. Garro and R. A. Vázquez, “Designing artificial neural networks using particle swarm optimization algorithms,” Computational intelligence and neuroscience , vol. 2015, 2015

2015

Earlier work this paper cites.

M. Mavrovouniotis and S. Yang, “Training neural networks with ant colony optimization algorithms for pattern classification,” Soft Computing , vol. 19, no. 6, pp. 1511–1522, 2015

2015

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Cited alongside, same era.

L. Kotthoff, C. Thornton, H. H. Hoos, F. Hutter, and K. Leyton-Brown, “Auto-weka 2.0: Automatic model selection and hyperparameter optimization in weka,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 826–830, 2017

2017

Cited alongside, same era.

V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proceedings of the IEEE , vol. 105, no. 12, pp. 2295–2329, 2017

2017

Cited alongside, same era.

R. S. Olson and J. H. Moore, “Tpot: A tree-based pipeline optimization tool for automating,” Automated Machine Learning: Methods, Systems, Challenges , p. 151, 2019

2019

Later among the works it cites.

T. Elsken, J. H. Metzen, F. Hutter et al. , “Neural architecture search.” 2019

2019

Later among the works it cites.

F. E. F. Junior and G. G. Yen, “Particle swarm optimization of deep neural networks architectures for image classification,” Swarm and Evolutionary Computation , vol. 49, pp. 62–74, 2019

2019

Later among the works it cites.

E. Byla and W. Pang, “Deepswarm: Optimising convolutional neural networks using swarm intelligence,” in UK Workshop on Computational Intelligence . Springer, 2019, pp. 119–130

2019

Later among the works it cites.

E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2019, pp. 113–123