Publications

M. Gillhofer, H. Ramsauer, J. Brandstetter, and S. Hochreiter (2019) A GAN based solver of black-box inverse problems. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019); e-print at openreview.net. (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)

M. Hofmarcher, T. Unterthiner, J. Arjona-Medina, G. Klambauer, S. Hochreiter, and B. Nessler (2019) Visual scene understanding for autonomous driving using semantic segmentation. in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer, 285, 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)

K. Preuer, G. Klambauer, F. Rippmann, S. Hochreiter, and T. Unterthiner (2019) Interpretable Deep Learning in Drug Discovery. in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer, 331, 2019-09-10; preprint at arXiv, 1903.02788v2. (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)

D. Jonietz & M. Kopp (2019) Towards Modeling Geographical Processes with Generative Adversarial Networks (GANs). 14th International Conference on Spatial Information Theory (COSIT 2019), Leibniz International Proceedings in Informatics (LIPIcs), 142, 27, 2019-09-03. (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)

M. P. Menden, D. Wang, M. J. Mason, B. Szalai, K. C. Bulusu, Y. Guan, T. Yu, J. Kang, M. Jeon, R. Wolfinger, T. Nguyen, M. Zaslavskiy, A.-S. D. C. D. Consortium, I. S. Jang, Z. Ghazoui, M. E. Ahsen, R. Vogel, E. C. Neto, T. Norman, E. K. Y. Tang, M. J. Garnett, G. Y. Di Veroli, S. Fawell, G. Stolovitzky, J. Guinney, J. R. Dry, and J. Saez-Rodriguez (2019) Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications, 10, 2674, 2019-06-17. (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)

G. Klambauer, S. Hochreiter, and M. Rarey (2019) Machine Learning in Drug Discovery. Journal of Chemical Information and Modeling , 59, 3, 945, 2019-03-25. (more) (download)

M. Hofmarcher, E. Rumetshofer, D.-A. Clevert, S. Hochreiter, and G. Klambauer (2019) Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks. Journal of Chemical Information and Modeling, 59, 3, 1163, 2019-03-06. (more) (download)

E. Rumetshofer, M. Hofmarcher, C. Röhrl, S. Hochreiter, and G. Klambauer (2019) Human-level Protein Localization with Convolutional Neural Networks. International Conference on Learning Representations, ICLR 2019, New Orleans, 6-9 May. (more) (download)