Publications

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)

T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2019) Quantum Optical Experiments Modeled by Long Short-Term Memory. arXiv, 1910.13804v1, Machine Learning (cs.LG), 2019-10-30. (more) (download)

J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler, and S. Hochreiter (2019) Patch Refinement – Localized 3D Object Detection. Machine Learning for Autonomous Driving Workshop at NeurIPS 2019, Vancouver, 10-12 Dec 2019, or preprint arXiv, 1910.04093v1, Computer Vision and Pattern Recognition (cs.CV), 2019-10-09. (more) (download)

M. Gillhofer, H. Ramsauer, J. Brandstetter, and S. Hochreiter (2019) A GAN based solver of black-box inverse problems. 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. NeurIPS 2019, Vancouver, 10-12 Dec 2019, or pre-print on arXiv, 1806.07857v3, Machine Learning (cs.LG), 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, doi.org/10.1007/978-3-030-28954-6_15, 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, doi.org/10.1007/978-3-030-28954-6_11, 2019-09-10. (more) (download)

D. Jonietz & M. Kopp (2019) Towards Modeling Geographical Processes with Generative Adversarial Networks (GANs). Proceedings of the 14th International Conference on Spatial Information Theory, COSIT 2019, Regensburg, Germany, Leibniz International Proceedings in Informatics (LIPIcs), 142: 27:1–27:9. (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, G. Shalev, G. Klambauer, S. Hochreiter, and G. Nearing (2019) Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling. arXiv, 1907.08456v1, Machine Learning (cs.LG), 2019-07-19. (more) (download)

M. P. Menden et al. (2019) Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications 10, 2674. (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. J. Chem. Inf. Model. 59, 945−946. (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, doi.org/10.1007/978-3-030-28954-6_19, see preprint at arXiv, 1903.07903v1, 2019-03-19. (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, doi.org/10.1007/978-3-030-28954-6_18, see preprint at arXiv, 1903.02788v2, 2019-03-18. (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. J. Chem. Inf. Model. 59, 3, 1163-1171. (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)