Gardner, E. Multiconnected neural network models. Journal of Physics A, 20(11):3453-3464, 1987. doi: 10.1088/0305-4470/20/11/046.
Gibbs, I. and Candes, E. J. Adaptive conformal inference under distribution shift. Advances in Neural Information Processing Systems, 34:1660-1672, 2021.
Gneiting, T. and Katzfuss, M. Probabilistic forecasting. Annual Review of Statistics and Its Application, 1(1):125-151, 2014. doi: 10.1146/annurev-statistics-062713-085831.
Hamilton, J. D. Analysis of time series subject to changes in regime. Journal of econometrics, 45 (1-2):39-70, 1990.
Herzen, J., Lässig, F., Piazzetta, S. G., Neuer, T., Tafti, L., Raille, G., Van Pottelbergh, T., Pasieka, M., Skrodzki, A., Huguenin, N., et al. Darts: User-friendly modern machine learning for time series. Journal of Machine Learning Research, 23(124):1-6, 2022.
Hochreiter, S. and Schmidhuber, J. Long short-term memory. Neural Comput., 9(8):1735-1780, 1997.
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79(8):2554-2558, 1982.
Hopfield, J. J. Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, 81(10):3088-3092, 1984. doi: 10.1073/pnas.81.10.3088.
Horn, D. and Usher, M. Capacities of multiconnected memory models. Journal of Physics France, 49(3):389-395, 1988. doi: 10.1051/jphys:01988004903038900.
Jensen, V., Bianchi, F. M., and Anfinsen, S. N. Ensemble conformalized quantile regression for probabilistic time series forecasting. arXiv preprint arXiv:2202.08756, 2022.
Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G. Uncertainty estimation with deep learning for rainfall-runoff modeling. Hydrology and Earth System Sciences, 26(6):1673-1693, 2022. doi: 10.5194/hess-26-1673-2022.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences, 23(12):5089-5110, 2019. doi: 10.5194/hess-23-5089-2019.
Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall-runoff modeling. Hydrology and Earth System Sciences, 25(5):2685-2703, 2021.
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D. NeuralHydrology — a Python library for deep learning research in hydrology. Journal of Open Source Software, 7(71):4050, 2022. doi: 10.21105/joss.04050.
Krotov, D. and Hopfield, J. J. Dense associative memory for pattern recognition. In Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I., and Garnett, R. (eds.), Advances in Neural Information Processing Systems, pp. 1172-1180. Curran Associates, Inc., 2016.
Krzysztofowicz, R. The case for probabilistic forecasting in hydrology. Journal of hydrology, 249 (1-4):2-9, 2001.
Lei, J. and Wasserman, L. A. Distribution-free prediction bands for non-parametric regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76, 2014.
Loshchilov, I. and Hutter, F. Decoupled weight decay regularization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019.