R.J.M.A. Triepels

Triepels, R. J. M. A., Segers, M. H. M., Rosen, P., Nuijts, R. M. M. A., van den Biggelaar, F. J. H. M., Henry, Y. P., Stenevi, U., Tassignon, M. J., Young, D., Behndig, A., Lundstroem, M., & Dickman, M. M. (2023). Development of machine learning models to predict posterior capsule rupture based on the EUREQUO registry. Acta Ophthalmologica, 101(6), 644-650. https://doi.org/10.1111/aos.15648

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Triepels, R. (2023). SASHA: Hyperparameter Optimization by Simulated Annealing and Successive Halving. In I. Maglogiannis, L. Iliadis, J. MacIntyre, & M. Dominguez (Eds.), Artificial Intelligence Applications and Innovations: 19th IFIP WG 12.5 International Conference, AIAI 2023 León, Spain, June 14-17, 2023 Proceedings, Part II (pp. 491-502). Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_39

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Triepels, R. J. M. A., Segers, M. H. M., Nuijts, R. M. M. A., Rosen, P., Henry, Y. P., Stenevi, U., Tassignon, M. J., Young, D., Behndig, A., Lundstrom, M., & Dickman, M. M. (2022). Prediction of posterior capsule rupture with machine learning based on the EUREQUO. Acta Ophthalmologica, 100(s268), 37-38.

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Triepels, R. (2021). Anomaly Detection by Robust Feature Reconstruction. In L. Iliadis, J. MacIntyre, C. Jayne, & E. Pimenidis (Eds.), Proceedings of the 22nd Engineering Applications of Neural Networks Conference : EANN 2021 (pp. 42-53). Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_4

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Triepels, R., Daniels, H., & Berndsen, R. (2021). Monitoring Liquidity Management of Banks With Recurrent Neural Networks. Computational Economics, 57(1), 89-112. https://doi.org/10.1007/s10614-020-10067-5

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R.J.M.A. Triepels