Publisher
Springer International Publishing
Reference20 articles.
1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 2623–2631 (2019)
2. Almogbel, M.A., Dang, A.H., Kameyama, W.: EEG-signals based cognitive workload detection of vehicle driver using deep learning. In: 2018 20th International Conference on Advanced Communication Technology (ICACT), pp. 256–259. IEEE (2018)
3. Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. Adv. Neural Inf. Process. Syst. 24 (2011)
4. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(2) (2012)
5. Blaiech, A.G., Ben Khalifa, K., Boubaker, M., Bedoui, M.H.: LVQ neural network optimized implementation on FPGA devices with multiple-word length operations for real-time systems. Neural Comput. Appl. 29(2), 509–528 (2018)
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Improving Deep Learning-Based Eye Movements Classification Using Bayesian Optimization;2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC);2023-11-09