Publications

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, 34, 5, 895–903, 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)

P. Herruzo & J. L. Larriba-Pey (2020) Recurrent Autoencoder with Skip Connections and Exogenous Variables for Traffic Forecasting. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:47-55, 2020-08-19. (more) (download)

H. Martin, D. Bucher, Y. Hong, R. Buffat, C. Rupprecht, and M. Raubal (2020) Graph-ResNets for short-term traffic forecasts in almost unknown cities. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123:153-163, 2020-08-19. (more) (download)

H. Ramsauer, B. Schäfl, J. Lehner, P. Seidl, M. Widrich, L. Gruber, M. Holzleitner, M. Pavlović, G. K. Sandve, V. Greiff, D. Kreil, M. Kopp, G. Klambauer, J. Brandstetter, and S. Hochreiter (2020) Hopfield Networks is All You Need. arXiv:2008.02217, 2020-08-06. (more) (download)

M. Widrich, B. Schäfl, H. Ramsauer, M. Pavlović, L. Gruber, M. Holzleitner, J. Brandstetter, G. K. Sandve, V. Greiff, S. Hochreiter, and G. Klambauer (2020) Modern Hopfield Networks and Attention for Immune Repertoire Classification. arXiv:2007.13505, 2020-07-16. (more) (download)

M. M. Cutchan, S. Özdal‐Oktay, and I. Giannopoulos (2020) Semantic‐based urban growth prediction. Transactions in GIS. 00: 1– 22. 2020-07-14. (more) (download)

A. Mitterecker, A. Hofmann, K. M. Trentino, A. Lloyd, M. F. Leahy, K. Schwarzbauer, T. Tschoellitsch, C. Böck, S. Hochreiter, and J. Meier (2020) Machine learning–based prediction of transfusion. Transfusion, 60, 1977–1986, 2020-06-28. (more) (download)

N. Sturm, A. Mayr, T. L. Van, V. Chupakhin, H. Ceulemans, J. Wegner, J.-F. Golib-Dzib, N. Jeliazkova, Y. Vandriessche, S. Böhm, V. Cima, J. Martinovic, N. Greene, T. V. Aa, T. J. Ashby, S. Hochreiter, O. Engkvist, G. Klambauer, and H. Chen (2020) Industry-scale application and evaluation of deep learning for drug target prediction. Journal of Cheminformatics, 12, 1-13, 2020-04-19. (more) (download)

M. Hofmarcher, A. Mayr, E. Rumetshofer, P. Ruch, P. Renz, J. Schimunek, P. Seidl, A. Vall, M. Widrich, S. Hochreiter, and G. Klambauer (2020) Large-Scale Ligand-Based Virtual Screening for SARS-CoV-2 Inhibitors Using Deep Neural Networks. SSRN 3561442, 2020-03-23. (more) (download)

A. Mayr, G. Klambauer, T. Unterthiner, and S. Hochreiter (2020) The LSC Benchmark Dataset: Technical Appendix and Partial Reanalysis. 2020-02-12. (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)

S. Kimeswenger, E. Rumetshofer, M. Hofmarcher, P. Tschandl, H. Kittler, S. Hochreiter, W. Hötzenecker, and G. Klambauer (2019) Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images. ML4H: Machine Learning for Health workshop at NeurIPS 2019, Vancouver, 10-12 Dec 2019, or preprint at arXiv, 1911.06616v3, Image and Video Processing (eess.IV), 2019-12-02. (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)

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)