Publications

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)

A. Vall, Y. Sabnis, J. Shi, R. Class, S. Hochreiter, and G. Klambauer (2021) The Promise of AI for DILI Prediction. Frontiers in Artificial Intelligence, 4, 638410, 2021-04-14. (more) (download)

T. Roland, C. Boeck, T. Tschoellitsch, A. Maletzky, S. Hochreiter, J. Meier, and G. Klambauer (2021) Machine Learning Based COVID-19 Diagnosis from Blood Tests with Robustness to Domain Shifts. medRxiv, 2021-04-09. (more) (download)

P. Seidl, P. Renz, N. Dyubankova, P. Neves, J. Verhoeven, J. K. Wegner, S. Hochreiter, and G. Klambauer (2021) Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction. arXiv:2104.03279, 2021-04-07. (more) (download)

B. Rasti, B. Koirala, P. Scheunders, and P. Ghamisi (2021) UnDIP: Hyperspectral Unmixing Using Deep Image Prior. IEEE Transactions on Geoscience and Remote Sensing, 2021-03-31. (more) (download)

P. M. Winter, S. Eder, J. Weissenböck, C. Schwald, T. Doms, T. Vogt, S. Hochreiter, and B. Nessler (2021) Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications. arXiv:2103.16910, 2021-03-31. (more) (download)

D. Klotz, F. Kratzert, M. Gauch, A. K. Sampson, G. Klambauer, S. Hochreiter, and G. Nearing (2021) Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling. Hydrology and Earth System Sciences, under review, 2021-03-15. (more) (download)

M. Pavlovic, L. Scheffer, K. Motwani, C. Kanduri, R. Kompova, N. Vazov, K. Waagan, F. L. Bernal, A. A. Costa, B. Corrie, R. Akbar, G. S. Al Hajj, G. Balaban, T. M. Brusko, M. Chernigovskaya, S. Christley, L. G. Cowell, R. Frank, I. Grytten, S. Gundersen, I. H. Haff, S. Hochreiter, E. Hovig, P.-H. Hsieh, G. Klambauer, M. L. Kuijjer, C. Lund-Andersen, A. Martini, T. Minotto, J. Pensar, K. Rand, E. Riccardi, P. A. Robert, A. Rocha, A. Slabodkin, I. Snapkov, L. M. Sollid, D. Titov, C. R. Weber, M. Widrich, G. Yaari, V. Greiff, and G. K. Sandve (2021) immuneML: an Ecosystem for Machine Learning Analysis of Adaptive Immune Receptor Repertoires. bioRxiv, 2021-03-15. (more) (download)

X. He, Y. Chen, and P. Ghamisi (2021) Dual Graph Convolutional Network for Hyperspectral Image Classification with Limited Training Samples. IEEE Transactions on Geoscience and Remote Sensing, 2021-03-08. (more) (download)

N. Yokoya, P. Ghamisi, R. Hansch, C. Prieur, H. Malha, J. Chanussot, C. Robinson, K. Malkin, and N. Jojic (2021) 2021 Data Fusion Contest: Geospatial Artificial Intelligence for Social Good. IEEE Geoscience and Remote Sensing Magazine, 9, 1, 287-C3, 2021-03-05. (more) (download)

C. Robinson, K. Malkin, N. Jojic, H. Chen, R. Qin, C. Xiao, M. Schmitt, P. Ghamisi, R. Hansch, and N. Yokoya (2021) Global Land Cover Mapping with Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021-03-04. (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)

J. Yue, L. Fang, H. Rahmani, and P. Ghamisi (2021) Self-Supervised Learning with Adaptive Distillation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 1-13, 2021-02-22. (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)

M. Gauch, D. Klotz, F. Kratzert, G. Nearing, S. Hochreiter, and a. J. Lin (2020) A Machine Learner’s Guide to Streamflow Prediction. NeurIPS Workshop: AI for Earth Sciences, 2020-12-12. (more) (download)

M. Holzleitner, L. Gruber, J. Arjona-Medina, J. Brandstetter, and S. Hochreiter (2020) Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER. arXiv:2012.01399, 2020-12-02. (more) (download)

L. Servadei, J. Zheng, J. Arjona-Medina, M. Werner, V. Esen, S. Hochreiter, W. Ecker, and R. Wille (2020) Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning. Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD, 37-42, 2020-11-16. (more) (download)

S. Kimeswenger, P. Tschandl, P. Noack, M. Hofmarcher, E. Rumetshofer, H. Kindermann, R. Silye, S. Hochreiter, M. Kaltenbrunner, E. Guenova, G. Klambauer, and W. Hoetzenecker (2020) Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns. Modern Pathology, 1-9, 2020-11-13. (more) (download)

P. Renz, D. Van Rompaey, J. K. Wegner, S. Hochreiter, and G. Klambauer (2020) On failure modes in molecule generation and optimization. Drug Discovery Today: Technologies, 32, 55-63, 2020-10-24. (more) (download)

T. Adler, J. Brandstetter, M. Widrich, A. Mayr, D. Kreil, M. Kopp, G. Klambauer, and S. Hochreiter (2020) Cross-Domain Few-Shot Learning by Representation Fusion. arXiv:2010.06498, 2020-10-13. (more) (download)

V. P. Patil, M. Hofmarcher, M.-C. Dinu, M. Dorfer, P. M. Blies, J. Brandstetter, J. A. Arjona-Medina, and S. Hochreiter (2020) Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution. arXiv:2009.14108, 2020-09-29. (more) (download)

D. P. Kreil, M. K. Kopp, D. Jonietz, M. Neun, A. Gruca, P. Herruzo, H. Martin, A. Soleymani, and S. Hochreiter (2020) The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task – Insights from the IARAI Traffic4cast Competition at NeurIPS 2019. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:232-241, 2020-08-19. (more) (download)