| An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... International Conference on Learning Representations (ICLR), 2021 | 38179 | 2021 |
| 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 | 12235 | 2017 |
| Fast and accurate deep network learning by exponential linear units (ELUs) DA Clevert, T Unterthiner, S Hochreiter International Conference on Learning Representations (ICLR), 2016 | 7066 | 2016 |
| Self-normalizing neural networks G Klambauer, T Unterthiner, A Mayr, S Hochreiter Advances in Neural Information Processing Systems (NeurIPS), 2017 | 3208 | 2017 |
| MLP-Mixer: An All-MLP Architecture for Vision I Tolstikhin, N Houlsby, A Kolesnikov, L Beyer, X Zhai, T Unterthiner, ... Advances in Neural Information Processing Systems (NeurIPS), 2021 | 2324 | 2021 |
| DeepTox: toxicity prediction using deep learning A Mayr, G Klambauer, T Unterthiner, S Hochreiter Frontiers in Environmental Science 3, 80, 2016 | 936 | 2016 |
| Do Vision Transformers See Like Convolutional Neural Networks? M Raghu, T Unterthiner, S Kornblith, C Zhang, A Dosovitskiy Advances in Neural Information Processing Systems (NeurIPS), 2021 | 885 | 2021 |
| Object-Centric Learning with Slot Attention F Locatello, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ... Advances in Neural Information Processing Systems (NeurIPS), 2020 | 686 | 2020 |
| 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 | 504 | 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 | 403 | 2018 |
| Understanding Robustness of Transformers for Image Classification S Bhojanapalli, A Chakrabarti, D Glasner, D Li, T Unterthiner, A Veit International Conference on Computer Vision (ICCV), 2021 | 374 | 2021 |
| 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 | 331 | 2016 |
| 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 | 318 | 2018 |
| & Houlsby, N.(2020). An image is worth 16x16 words: Transformers for image recognition at scale A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... arXiv preprint arXiv:2010.11929, 2010 | 295 | 2010 |
| 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 | 238 | 2014 |
| Rudder: Return decomposition for delayed rewards JA Arjona-Medina, M Gillhofer, M Widrich, T Unterthiner, J Brandstetter, ... Advances in Neural Information Processing Systems (NeurIPS), 2018 | 233 | 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 | 128 | 2019 |
| Toxicity prediction using deep learning T Unterthiner, A Mayr, G Klambauer, S Hochreiter arXiv preprint arXiv:1503.01445, 2015 | 122 | 2015 |
| 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 | 107 | 2015 |
| International Conference on Learning Representations A Dosovitskiy, L Beyer, A Kolesnikov, D Weissenborn, X Zhai, ... ICLR 2010, 11929, 2021 | 98 | 2021 |
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University Linz在 ml.jku.at 的电子邮件经过验证
