A Theory of AI
LSTMs
Publications
2021
F. Kratzert, M. Gauch, G. Nearing, S. Hochreiter, and D. Klotz (2021) Rainfall-Runoff Modeling with Long Short-Term Memory Networks (LSTM)—an Overview. Österreichische Wasser-und Abfallwirtschaft, 2021-05-17. (more) (download)
M. Gauch, F. Kratzert, D. Klotz, G. Nearing, J. Lin, and S. Hochreiter (2021) Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network. Hydrology and Earth System Sciences, 25, 4, 2045-2062, 2021-04-19. (more) (download)
M. Gauch, F. Kratzert, G. Nearing, J. Lin, S. Hochreiter, J. Brandstetter, and D. Klotz (2021) Multi-Timescale LSTM for Rainfall–Runoff Forecasting. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9714, 2021-03-03. (more) (download)
P.-J. Hoedt, F. Kratzert, D. Klotz, C. Halmich, M. Holzleitner, G. Nearing, S. Hochreiter, and G. Klambauer (2021) MC-LSTM: Mass-Conserving LSTM. arXiv:2101.05186, 2021-01-13. (more) (download)
2019
L. Arras, J. Arjona-Medina, M. Widrich, G. Montavon, M. Gillhofer, K.-R. Müller, S. Hochreiter, and W. Samek (2019) Explaining and Interpreting LSTMs. in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer, 211, 2019-09-10; preprint at arXiv:1909.12114. (more) (download)