[1] Y. LeCun, Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444
[2] Ru Zhang, Wencong Xiao, Hongyu Zhang, Yu Liu, Haoxiang Lin, and Mao Yang. 2020. An Empirical Study on Program Failures of Deep Learning Jobs. In Proceedings of the 42nd International Conference on Software Engineering (Seoul, Republic of Korea) (ICSE ’20). Association for Computing Machinery, NY, USA, 1159–1170.
[3] Çiçek, Özgün, et al. "3D U-Net: learning dense volumetric segmentation from sparse annotation." International conference on medical image computing and computer-assisted intervention. Springer, Cham, 2016.
[4] Chenna, Sai P., Greg Stitt, and Herman Lam. "Multi-parameter performance modeling using symbolic regression." 2019 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2019.
[5] J. R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA, USA: MIT Press, 1994.
[6] J. McDermott, D. R. White, S. Luke, L. Manzoni, M. Castelli, L. Vanneschi, W. Jaskowski, K. Krawiec, R. Harper, K. De Jong, and U.-M. O’Reilly, “Genetic programming needs better benchmarks,” in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, ser. GECCO ’12. New York, NY, USA: ACM, 2012, pp. 791–798. [Online]. Available: http://doi.acm.org/10.1145/2330163.2330273.
[7] M. Schmidt and H. Lipson, “Distilling free-form natural laws from experimental data,” Science, vol. 324, no. 5923, pp. 81–85, 2009. [Online]. Available: http://science.sciencemag.org/content/324/5923/81.
[8] Stitt, Greg, and David Campbell. "PANDORA: An Architecture-Independent Parallelizing Approximation-Discovery Framework." ACM Transactions on Embedded Computing Systems (TECS) 19.5 (2020): 1-17.
[9] F.-A. Fortin, F.-M. De Rainville, M.-A. Gardner, M. Parizeau, and C. Gagné, “DEAP: Evolutionary algorithms made easy,” Journal of Machine Learning Research, vol. 13, pp. 2171–2175, jul 2012.
[10] D. P. Searson, D. E. Leahy, and M. J. Willis, “Gptips: An open source genetic programming toolbox for multigene symbolic regression.”
[11] G. S. Hornby, “ALPS: The age-layered population structure for reducing the problem of premature convergence,” in Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, ser. GECCO ’06. New York, NY, USA: ACM, 2006, pp. 815–822. [Online]. Available: http://doi.acm.org/10.1145/1143997.1144142.
[12] H.-G. Beyer and H.-P. Schwefel, “Evolution strategies – a comprehensive introduction,” Natural Computing, vol. 1, no. 1, pp. 3–52, Mar 2002. [Online]. Available: https://doi.org/10.1023/A:1015059928466.
[13] S. Luke and L. Panait, “Fighting bloat with nonparametric parsimony pressure,” in Parallel Problem Solving from Nature — PPSN VII, J. J. M. Guervós, P. Adamidis, H.-G. Beyer, H.-P. Schwefel, and J.-L. Fernández-Villacañas, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002, pp. 411–421.
[14] F. G. Lobo, C. F. Lima, and Z. Michalewicz, Parameter Setting in Evolutionary Algorithms, 1st ed. Springer Publishing Company, Incorporated, 2007.
[15] M. Kommenda, G. Kronberger, S. Winkler, M. Affenzeller, and S. Wagner, “Effects of constant optimization by nonlinear least squares minimization in symbolic regression,” in Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, ser. GECCO ’13 Companion. New York, NY, USA: ACM, 2013, pp. 1121–1128. [Online]. Available: http://doi.acm.org/10.1145/2464576.2482691.
[16] Malakar, Preeti, et al. "Benchmarking machine learning methods for performance modeling of scientific applications." 2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). IEEE, 2018.
[17] C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning. Springer, 2006, vol. 4, no. 4.
[18] A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and computing, vol. 14, no. 3, pp. 199–222, 2004.
[19] W.-Y. Loh, “Classification and regression trees,” Wiley interdisciplinary reviews: data mining and knowledge discovery, vol. 1, no. 1, pp. 14–23, 2011.
[20] M. A. Heroux, D. W. Doerfler, P. S. Crozier, J. M. Willenbring, H. C. Edwards, A. Williams, M. Rajan, E. R. Keiter, H. K. Thornquist, and R. W. Numrich, “Improving Performance via Mini-applications,” Sandia National Laboratories, Tech. Rep. SAND2009-5574, 2009.
[21] Chenna, Sai P., et al. "Scalable Performance Prediction of Irregular Workloads in Multi-Phase Particle-in-Cell Applications." 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2021.
[22] M. A. Heroux, D. W. Doerfler, P. S. Crozier, J. M. Willenbring, H. C. Edwards, A. Williams, M. Rajan, E. R. Keiter, H. K. Thornquist, and R. W. Numrich, “Improving Performance via Mini-applications,” Sandia National Laboratories, Tech. Rep. SAND2009-5574, 2009.
[23] I. Karlin, J. Keasler, and R. Neely, “Lulesh 2.0 updates and changes,” Tech. Rep. LLNL-TR-641973, August 2013.
[24] Banerjee, Tania, et al. "Cmt-bone—a proxy application for compressible multiphase turbulent flows." 2016 IEEE 23rd International Conference on High Performance Computing (HiPC). IEEE, 2016.