Dr Hochreiter is a pioneer in the field of Artificial Intelligence (AI). He was the first to identify the key obstacle to Deep Learning and then discovered a general approach to address this challenge. He thus became the founding father of modern Deep Learning and AI.
He is also a professor at Johannes Kepler University Linz.
Publications
2021
A. Gruca, P. Herruzo, P. Rípodas, A. Kucik, C. Briese, M. K. Kopp, S. Hochreiter, P. Ghamisi, and D. P. Kreil (2021) CDCEO’21 – First Workshop on Complex Data Challenges in Earth Observation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), November 1–5, 2021, Virtual Event, QLD, Australia. ACM, New York, NY, USA. (in press). (more) (download)
M. Kopp, D. Kreil, M. Neun, D. Jonietz, H. Martin, P. Herruzo, A. Gruca, A. Soleymani, F. Wu, Y. Liu, J. Xu, J. Zhang, J. Santokhi, A. Bojesomo, H. Al Marzouqi, P. Liatsis, P. H. Kwok, Q. Qi, and S. Hochreiter (2021) Traffic4cast at NeurIPS 2020 – Yet More on the Unreasonable Effectiveness of Gridded Geo-Spatial Processes. Proceedings of the NeurIPS 2020 Competition and Demonstration Track, PMLR, 133, 325-343, 2021. (more) (download)
P. A. Robert, R. Akbar, R. Frank, M. Pavlović, M. Widrich, I. Snapkov, M. Chernigovskaya, L. Scheffer, A. Slabodkin, B. B. Mehta, M. Ha Vu, A. Prósz, K. Abram, A. Olar, E. Miho, D. T. T. Haug, F. Lund-Johansen, S. Hochreiter, I. H. Haff, G. Klambauer, G. K. Sandve, and V. Greiff (2021) One Billion Synthetic 3D-Antibody-Antigen Complexes Enable Unconstrained Machine-Learning Formalized Investigation of Antibody Specificity Prediction. bioRxiv, doi:10.1101/2021.07.06.451258, 2021-07-11. (more) (download)
R. Akbar, P. A. Robert, C. R. Weber, M. Widrich, R. Frank, M. Pavlović, L. Scheffer, M. Chernigovskaya, I. Snapkov, A. Slabodkin, B. B. Mehta, E. Miho, F. Lund-Johansen, J. Andersen, S. Hochreiter, I. H. Haff, G. Klambauer, G. K. Sandve, and V. Greiff (2021) In Silico Proof of Principle of Machine Learning-Based Antibody Design at Unconstrained Scale. bioRxiv, doi:10.1101/2021.07.08.451480, 2021-07-09. (more) (download)
A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2021) Boundary Graph Neural Networks for 3D Simulations. arXiv:2106.11299, 2021-06-21. (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)
A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2021) Learning 3D Granular Flow Simulations. arXiv: 2105.01636, 2021-05-04. (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)
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)
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. LM 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)
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)
2020
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, 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)
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)
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. Le 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)
2019
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)
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)
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. (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)