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
2022
A. Maletzky, C. Böck, T. Tschoellitsch, T. Roland, H. Ludwig, S. Thumfart, M. Giretzlehner, S. Hochreiter, and J. Meier (2022) Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities. JMIR Medical Informatics, 10, 10, e38557, 2022-10-21. (more) (download)
M. Gauch, F. Kratzert, O. Gilon, H. Gupta, J. Mai, G. Nearing, B. Tolson, S. Hochreiter, and D. Klotz (2022) In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance. EarthArXiv, 2022-10-19. (more) (download)
Y. Xu, W. Yu, P. Ghamisi, M. Kopp, and S. Hochreiter (2022) Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks. arXiv:2208.04441, 2022-08-08. (more) (download)
P. Ghamisi, O. Ghorbanzadeh, Y. Xu, P. Herruzo, D. Kreil, M. Kopp, and S. Hochreiter (2022) The Landslide4Sense Competition 2022. CEUR Workshop Proceedings, 3207, 2022-07-25. (more) (download)
C. Eichenberger, M. Neun, H. Martin, P. Herruzo, M. Spanring, Y. Lu, S. Choi, V. Konyakhin, N. Lukashina, A. Shpilman, N. Wiedemann, M. Raubal, B. Wang, H. L. Vu, R. Mohajerpoor, C. Cai, I. Kim, L. Hermes, A. Melnik, R. Velioglu, M. Vieth, M. Schilling, A. Bojesomo, H. Al Marzouqi, P. Liatsis, J. Santokhi, D. Hillier, Y. Yang, J. Sarwar, A. Jordan, E. Hewage, D. Jonietz, F. Tang, A. Gruca, M. Kopp, D. Kreil, and S. Hochreiter (2022) Traffic4cast at NeurIPS 2021 – Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes. NeurIPS 2021 Competitions and Demonstrations Track, PMLR, 176, 97-112, 2022-07-20. (more) (download)
C. Steinparz, T. Schmied, F. Paischer, M.-C. Dinu, V. Patil, A. Bitto-Nemling, H. Eghbal-zadeh, and S. Hochreiter (2022) Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning. arXiv:2207.05742, 2022-07-12. (more) (download)
M. Gauch, M. Beck, T. Adler, D. Kotsur, S. Fiel, H. Eghbal-zadeh, J. Brandstetter, J. Kofler, M. Holzleitner, W. Zellinger, D. Klotz, S. Hochreiter, and S. Lehner (2022) Few-Shot Learning by Dimensionality Reduction in Gradient Space. arXiv:2206.03483, 2022-06-07. (more) (download)
B. Schäfl, L. Gruber, A. Bitto-Nemling, and S. Hochreiter (2022) Hopular: Modern Hopfield Networks for Tabular Data. arXiv:2206.00664, 2022-06-01. (more) (download)
F. Paischer, T. Adler, V. Patil, A. Bitto-Nemling, M. Holzleitner, S. Lehner, H. Eghbal-zadeh, and S. Hochreiter (2022) History Compression via Language Models in Reinforcement Learning. arXiv:2205.12258, 2022-05-24. (more) (download)
D. Klotz, M. Gauch, G. Nearing, S. Hochreiter, and F. Kratzert (2022) Deficiencies in Hydrological Modelling Practices. EGU22-12403, 2022-05-23. (more) (download)
A. Sanchez-Fernandez, E. Rumetshofer, S. Hochreiter, and G. Klambauer (2022) Contrastive Learning of Image- and Structure-based Representations in Drug Discovery. ICLR 2022 Machine Learning for Drug Discovery Workshop Proceedings, 2022-04-29. (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. T. Andersen, S. Hochreiter, I. H. Haff, G. Klambauer, G. K. Sandve, and V. Greiff (2022) In Silico Proof of Principle of Machine Learning-Based Antibody Design at Unconstrained Scale. mAbs, 14, 1, 2031482, 2022-04-04. (more) (download)
D. Klotz, F. Kratzert, M. Gauch, A. K. Sampson, J. Brandstetter, G. Klambauer, S. Hochreiter, and G. Nearing (2022) Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling. Hydrology and Earth System Sciences, 26, 6, 1673-1693, 2022-03-31. (more) (download)
T. Roland, C. Boeck, T. Tschoellitsch, A. Maletzky, S. Hochreiter, J. Meier, and G. Klambauer (2022) Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. Journal of Medical Systems, 46, 5, 23, 2022-03-29. (more) (download)
S. Hochreiter (2022) Toward a broad AI. Communications of the ACM, 65, 4, 56-57, 2022-03-19. (more) (download)
L. Servadei, J. H. Lee, J. A. A. Medina, M. Werner, S. Hochreiter, W. Ecker, and R. Wille (2022) Deep Reinforcement Learning for Optimization at Early Design Stages. IEEE Design & Test, 2022-01-20. (more) (download)
P. Seidl, P. Renz, N. Dyubankova, P. Neves, J. Verhoeven, J. K. Wegner, M. Segler, S. Hochreiter, and a. G. Klambauer (2022) Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks. Journal of Chemical Information and Modeling, 62, 9, 2111, 2022-01-15. (more) (download)
K. M. Trentino, K. Schwarzbauer, A. Mitterecker, A. Hofmann, A. Lloyd, M. F. Leahy, T. Tschoellitsch, C. Böck, S. Hochreiter, and J. Meier (2022) Machine Learning-Based Mortality Prediction of Patients at Risk During Hospital Admission. Journal of Patient Safety, 2022-01-12. (more) (download)
2021
P. Herruzo, A. Gruca, L. Lliso, X. Calbet, P. Rípodas, S. Hochreiter, M. Kopp, and D. P. Kreil (2021) High-Resolution Multi-Channel Weather Forecasting – First Insights on Transfer Learning from the Weather4cast Competitions 2021. 2021 IEEE International Conference on Big Data (Big Data), 5750-5757, 2021-12-15. (more) (download)
T. Adler, M. Erhard, M. Krenn, J. Brandstetter, J. Kofler, and S. Hochreiter (2021) Quantum Optical Experiments Modeled by Long Short-Term Memory. Photonics, 8, 12, 535, 2021-11-26. (more) (download)
K. Schweighofer, M. Hofmarcher, M.-C. Dinu, P. Renz, A. Bitto-Nemling, V. Patil, and S. Hochreiter (2021) Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning. arXiv:2111.04714, 2021-11-08. (more) (download)
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. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 4878–4879, 2021-10-26. (more) (download)
A. Fürst, E. Rumetshofer, V. Tran, H. Ramsauer, F. Tang, J. Lehner, D. Kreil, M. Kopp, G. Klambauer, A. Bitto-Nemling, and S. Hochreiter (2021) CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. arXiv:2110.11316, 2021-10-21. (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)
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)
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)
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)
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. 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)
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)