| GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium M Heusel, H Ramsauer, T Unterthiner, B Nessler, G Klambauer, ... https://arxiv.org/abs/1706.08500v4, 2017 | 14451* | 2017 |
| Self-Normalizing Neural Networks G Klambauer, T Unterthiner, A Mayr, S Hochreiter Advances in Neural Information Processing Systems 30, 972--981, 2017 | 3505 | 2017 |
| DeepTox: toxicity prediction using deep learning A Mayr, G Klambauer, T Unterthiner, S Hochreiter Frontiers in Environmental Science 3, 80, 2016 | 1030 | 2016 |
| Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F Kratzert, D Klotz, G Shalev, G Klambauer, S Hochreiter, G Nearing Hydrology and Earth System Sciences 23 (12), 5089-5110, 2019 | 559 | 2019 |
| Large-scale comparison of machine learning methods for drug target prediction on ChEMBL A Mayr, G Klambauer, T Unterthiner, M Steijaert, JK Wegner, ... Chemical science 9 (24), 5441-5451, 2018 | 551 | 2018 |
| Hopfield networks is all you need H Ramsauer, B Schäfl, J Lehner, P Seidl, M Widrich, T Adler, L Gruber, ... arXiv preprint arXiv:2008.02217, 2020 | 549 | 2020 |
| DeepSynergy: predicting anti-cancer drug synergy with Deep Learning K Preuer, RPI Lewis, S Hochreiter, A Bender, KC Bulusu, G Klambauer Bioinformatics 34 (9), 1538-1546, 2018 | 512 | 2018 |
| cn. MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate G Klambauer, K Schwarzbauer, A Mayr, DA Clevert, A Mitterecker, ... Nucleic acids research 40 (9), e69, 2012 | 506 | 2012 |
| Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery K Preuer, P Renz, T Unterthiner, S Hochreiter, G Klambauer Journal of chemical information and modeling 58 (9), 1736-1741, 2018 | 365 | 2018 |
| Deep learning as an opportunity in virtual screening T Unterthiner, A Mayr, G Klambauer, M Steijaert, JK Wegner, ... Deep Learning and Representation Learning Workshop, NIPS 2014, 2014 | 248 | 2014 |
| Repurposed high-throughput image assays enables biological activity prediction for drug discovery J Simm, G Klambauer, A Arany, M Steijaert, JK Wegner, E Gustin, ... Cell Chemical Biology, 108399, 2018 | 239 | 2018 |
| Graph networks for molecular design R Mercado, T Rastemo, E Lindelöf, G Klambauer, O Engkvist, H Chen, ... Machine Learning: Science and Technology 2 (2), 025023, 2021 | 205 | 2021 |
| How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology C Wittwehr, H Aladjov, G Ankley, HJ Byrne, J de Knecht, E Heinzle, ... Toxicological Sciences 155 (2), 326-336, 2017 | 175 | 2017 |
| Modern hopfield networks and attention for immune repertoire classification M Widrich, B Schäfl, M Pavlović, H Ramsauer, L Gruber, M Holzleitner, ... Advances in neural information processing systems 33, 18832-18845, 2020 | 141 | 2020 |
| Interpretable deep learning in drug discovery K Preuer, G Klambauer, F Rippmann, S Hochreiter, T Unterthiner Explainable AI: interpreting, explaining and visualizing deep learning, 331-345, 2019 | 141 | 2019 |
| On failure modes in molecule generation and optimization P Renz, D Van Rompaey, JK Wegner, S Hochreiter, G Klambauer Drug Discovery Today: Technologies 32, 55-63, 2019 | 131 | 2019 |
| Toxicity prediction using deep learning T Unterthiner, A Mayr, G Klambauer, S Hochreiter arXiv preprint arXiv:1503.01445, 2015 | 129 | 2015 |
| NeuralHydrology–interpreting LSTMs in hydrology F Kratzert, M Herrnegger, D Klotz, S Hochreiter, G Klambauer Explainable AI: Interpreting, explaining and visualizing deep learning, 347-362, 2019 | 126 | 2019 |
| Prediction of human population responses to toxic compounds by a collaborative competition F Eduati, LM Mangravite, T Wang, H Tang, S Hochreiter, G Klambauer, ... Nature biotechnology, 2015 | 121 | 2015 |
| Cloob: Modern hopfield networks with infoloob outperform clip A Fürst, E Rumetshofer, J Lehner, VT Tran, F Tang, H Ramsauer, D Kreil, ... Advances in neural information processing systems 35, 20450-20468, 2022 | 119 | 2022 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
