| Fast and accurate deep network learning by exponential linear units (ELUs) DA Clevert, T Unterthiner, S Hochreiter International Conference on Learning Representations (ICLR), 2016 | 2858 | 2016 |
| GANs trained by a two time-scale update rule converge to a local nash equilibrium M Heusel, H Ramsauer, T Unterthiner, B Nessler, S Hochreiter Advances in Neural Information Processing Systems, 6626-6637, 2017 | 2028 | 2017 |
| Self-normalizing neural networks G Klambauer, T Unterthiner, A Mayr, S Hochreiter Advances in neural information processing systems, 971-980, 2017 | 1148 | 2017 |
| DeepTox: toxicity prediction using deep learning A Mayr, G Klambauer, T Unterthiner, S Hochreiter Frontiers in Environmental Science 3, 80, 2016 | 359 | 2016 |
| 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 | 149 | 2018 |
| Deep Learning as an Opportunity in Virtual Screening T Unterthiner, A Mayr, G ünter Klambauer, M Steijaert, J Wenger, ... Deep Learning and Representation Learning Workshop (NIPS 2014), 2014 | 142 | 2014 |
| Speeding up Semantic Segmentation for Autonomous Driving M Treml, J Arjona-Medina, T Unterthiner, R Durgesh, F Friedmann, ... Workshop on Machine Learning for Intelligent Transportation Systems (NIPS 2016), 2016 | 124 | 2016 |
| Toxicity prediction using deep learning T Unterthiner, A Mayr, G Klambauer, S Hochreiter arXiv preprint arXiv:1503.01445, 2015 | 70 | 2015 |
| 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 | 63 | 2018 |
| Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project B Verbist, G Klambauer, L Vervoort, W Talloen, Z Shkedy, O Thas, ... Drug discovery today 20 (5), 505-513, 2015 | 62 | 2015 |
| Rudder: Return decomposition for delayed rewards JA Arjona-Medina, M Gillhofer, M Widrich, T Unterthiner, J Brandstetter, ... Advances in Neural Information Processing Systems, 13566-13577, 2019 | 51 | 2019 |
| Coulomb GANs: Provably optimal nash equilibria via potential fields T Unterthiner, B Nessler, G Klambauer, M Heusel, H Ramsauer, ... International Conference on Learning Representations (ICLR), 2018 | 45 | 2018 |
| Towards accurate generative models of video: A new metric & challenges T Unterthiner, S van Steenkiste, K Kurach, R Marinier, M Michalski, ... arXiv preprint arXiv:1812.01717, 2018 | 32 | 2018 |
| 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 | 29 | 2019 |
| DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions G Klambauer, T Unterthiner, S Hochreiter Nucleic acids research 41 (21), e198-e198, 2013 | 28 | 2013 |
| Deep Learning for Drug Target Prediction T Unterthiner, A Mayr, G Klambauer, M Steijaert, H Ceulemans, J Wenger, ... Workshop on Representation and Learning Methods for Complex Outputs (NIPS2014), 2014 | 26* | 2014 |
| SCIKIT-CUDA 0.5. 1: A Python interface to GPU-powered libraries LE Givon, T Unterthiner, NB Erichson, DW Chiang, E Larson, L Pfister, ... | 20* | 2015 |
| Rectified factor networks DA Clevert, A Mayr, T Unterthiner, S Hochreiter Advances in neural information processing systems, 1855-1863, 2015 | 17 | 2015 |
| Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map G Klambauer, M Wischenbart, M Mahr, T Unterthiner, A Mayr, ... Bioinformatics 31 (20), 3392-3394, 2015 | 15 | 2015 |
| ELU-networks: fast and accurate CNN learning on imagenet M Heusel, DA Clevert, G Klambauer, A Mayr, K Schwarzbauer, ... NiN 8, 35.68, 2015 | 8 | 2015 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
