Long Short-Term Memory

S. Sanokowski, W. Berghammer, J. Kofler, S. Hochreiter, and S. Lehner (2022) One Network to Approximate Them All: Amortized Variational Inference of Ising Ground States. Machine Learning and the Physical Sciences at NeurIPS 2022, 2022-12-03. (more) (download)

D. Klotz, F. Kratzert, M. Gauch, A. K. Sampson, J. Brandstetter, G. Klambauer, S. Hochreiter, and G. Nearing (2022) Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling. Hydrology and Earth System Sciences, 26, 6, 1673-1693, 2022-03-31. (more) (download)

T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2021) Quantum Optical Experiments Modeled by Long Short-Term Memory. Photonics, 8, 12, 535, 2021-11-26. (more) (download)

F. Kratzert, D. Klotz, S. Hochreiter, and G. Nearing (2021) A Note on Leveraging Synergy in Multiple Meteorological Datasets with Deep Learning for Rainfall-Runoff Modeling. Hydrology and Earth System Sciences, 25, 5, 2685-2703, 2021-05-20. (more) (download)

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)

F. Kratzert, D. Klotz, M. Gauch, C. Klingler, G. Nearing, and S. Hochreiter (2021) Large-Scale River Network Modeling Using Graph Neural Networks. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13375, 2021-03-03. (more) (download)

D. Klotz, F. Kratzert, M. Gauch, A. K. Sampson, G. Klambauer, J. Brandstetter, S. Hochreiter, and G. Nearing (2021) Uncertainty Estimation with LSTM Based Rainfall-Runoff Models. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13308, 2021-03-03. (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)

F. Kratzert, D. Klotz, M. Herrnegger, A. K. Sampson, S. Hochreiter, and G. S. Nearing (2019) Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning. Water Resources Research. 55, 12, 11344-11354. 2019-12-23. (more) (download)

F. Kratzert, D. Klotz, G. Shalev, G. Klambauer, S. Hochreiter, and G. Nearing (2019) 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, 2019-12-17. (more) (download)

F. Kratzert, D. Klotz, G. Klambauer, S. Hochreiter, and G. S. Nearing (2019) Large-Scale Rainfall-Runoff Modeling using the Long Short-Term Memory Network. American Geophysical Union, AGU Fall Meeting 2019, San Francisco, 9-13 Dec. (more) (download)

F. Kratzert, D. Klotz, J. Brandstetter, P.-J. Hoedt, G. Nearing, and S. Hochreiter (2019) Using LSTMs for climate change assessment studies on droughts and floods. arXiv, 1911.03941v2, Machine Learning (cs.LG), 2019-11-28. (more) (download)

T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2019) LSTM-Designed Quantum Experiments. Machine Learning and the Physical Sciences Workshop at NeurIPS 2019, Vancouver, 10-12 Dec 2019. (more) (download)

J. A. Arjona-Medina, M. Gillhofer, M. Widrich, T. Unterthiner, J. Brandstetter, and S. Hochreiter (2019) RUDDER – Return Decomposition with Delayed Rewards. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 13566;  e-print also at arXiv:1806.07857v3, 2019-09-10. (more) (download)

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)

F. Kratzert, M. Herrnegger, D. Klotz, S. Hochreiter, and G. Klambauer (2019) NeuralHydrology – Interpreting LSTMs in Hydrology. in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer, 347, 2019-09-10; preprint at arXiv:1903.07903v2. (more) (download)

F. Kratzert, D. Klotz, A. K. Sampson, S. Hochreiter, and G. Nearing (2019) Prediction in Ungauged Basins with Long Short-term Memory Networks. EarthArXiv. doi:10.31223/osf.io/4rysp, 2019-08-26. (more) (download)

F. Kratzert, D. Klotz, M. Herrnegger, S. Hochreiter, and G. Klambauer (2019) Using large data sets towards generating a catchment aware hydrological model for global applications. Geophysical Research Abstracts, Vol. 21, EGU2019-13795. EGU General Assembly 2019. (more) (download)

D. Klotz, F. Kratzert, M. Herrnegger, S. Hochreiter, and G. Klambauer (2019) Towards the quantification of uncertainty for deep learning based rainfall-runoff models. Geophysical Research Abstracts, Vol. 21, EGU2019-10708-2. EGU General Assembly 2019. (more) (download)