HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization for Neural Networks

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J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” in Advances in neural information processing systems , 2012, pp. 2951–2959

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J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research , vol. 13, no. Feb, pp. 281–305, 2012

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Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” arXiv preprint arXiv:1408.5093 , 2014

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M. A. Gelbart, J. Snoek, and R. P. Adams, “Bayesian optimization with unknown constraints,” arXiv preprint arXiv:1403.5607 , 2014

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S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in Advances in Neural Information Processing Systems , 2015, pp. 1135–1143

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D. Stamoulis, D. Rodopoulos, B. H. Meyer, D. Soudris, F. Catthoor, and Z. Zilic, “Efficient reliability analysis of processor datapath using atomistic bti variability models,” in Proceedings of the 25th edition on Great Lakes Symposium on VLSI . ACM, 2015, pp. 57–62

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T. Domhan, J. T. Springenberg, and F. Hutter, “Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves.” in IJCAI , 2015, pp. 3460–3468

2015

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B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas, “Taking the human out of the loop: A review of bayesian optimization,” Proceedings of the IEEE , vol. 104, no. 1, pp. 148–175, 2016

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D. Stamoulis and D. Marculescu, “Can we guarantee performance requirements under workload and process variations?” in Proceedings of the 2016 International Symposium on Low Power Electronics and Design . ACM, 2016, pp. 308–313

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E. Cai, D. Stamoulis, and D. Marculescu, “Exploring aging deceleration in finfet-based multi-core systems,” in Computer-Aided Design (ICCAD), 2016 IEEE/ACM International Conference on . IEEE, 2016, pp. 1–8

2016

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J. M. Hernández-Lobato, M. A. Gelbart, R. P. Adams, M. W. Hoffman, and Z. Ghahramani, “A general framework for constrained bayesian optimization using information-based search,” 2016

2016

Later among the works it cites.

J. M. Hernández-Lobato, M. A. Gelbart, B. Reagen, R. Adolf, D. Hernández-Lobato, P. N. Whatmough, D. Brooks, G.-Y. Wei, and R. P. Adams, “Designing neural network hardware accelerators with decoupled objective evaluations,” in NIPS workshop on Bayesian Optimization , 2016, p. l0

2016