| Towards the first adversarially robust neural network model on MNIST L Schott, J Rauber, M Bethge, W Brendel International Conference on Learning Representations 2019, 2018 | 437 | 2018 |
| Comparative study of deep learning software frameworks S Bahrampour, N Ramakrishnan, L Schott, M Shah arXiv preprint arXiv:1511.06435, 2015 | 224 | 2015 |
| A simple way to make neural networks robust against diverse image corruptions E Rusak, L Schott, RS Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 212 | 2020 |
| Comparative study of caffe, neon, theano, and torch for deep learning S Bahrampour, N Ramakrishnan, L Schott, M Shah | 143 | 2016 |
| Towards nonlinear disentanglement in natural data with temporal sparse coding D Klindt, L Schott, Y Sharma, I Ustyuzhaninov, W Brendel, M Bethge, ... arXiv preprint arXiv:2007.10930, 2020 | 123 | 2020 |
| Visual representation learning does not generalize strongly within the same domain L Schott, J Von Kügelgen, F Träuble, P Gehler, C Russell, M Bethge, ... arXiv preprint arXiv:2107.08221, 2021 | 60 | 2021 |
| Learned watershed: End-to-end learning of seeded segmentation S Wolf, L Schott, U Kothe, F Hamprecht Proceedings of the IEEE International Conference on Computer Vision, 2011-2019, 2017 | 52 | 2017 |
| Increasing the robustness of dnns against im-age corruptions by playing the game of noise E Rusak, L Schott, R Zimmermann, J Bitterwolfb, O Bringmann, M Bethge, ... | 51 | 2020 |
| Score-based generative classifiers RS Zimmermann, L Schott, Y Song, BA Dunn, DA Klindt NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021 | 48 | 2021 |
| Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction S Zhang, S Bahrampour, N Ramakrishnan, L Schott, M Shah International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016 | 26 | 2016 |
| Towards the first adversarially robust neural network model on mnist. 2019 L Schott, J Rauber, W Brendel, M Bethge URL https://arxiv. org/pdf/1805.09190. pdf, 2018 | 8 | 2018 |
| Comparative study of Caffe S Bahrampour, N Ramakrishnan, L Schott, M Shah Neon, Theano, and Torch for Deep Learning. arXiv 1511, 2015 | 6 | 2015 |
| Understanding neural coding on latent manifolds by sharing features and dividing ensembles M Bjerke, L Schott, KT Jensen, C Battistin, DA Klindt, BA Dunn arXiv preprint arXiv:2210.03155, 2022 | 5 | 2022 |
| Comparative study of deep learning software frameworks. arXiv 2015 S Bahrampour, N Ramakrishnan, L Schott, M Shah arXiv preprint arXiv:1511.06435 3, 0 | 5 | |
| Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data L Hennicke, CM Adriano, H Giese, JM Koehler, L Schott arXiv preprint arXiv:2405.03243, 2024 | 1 | 2024 |
| Challenging Common Assumptions in Multi-task Learning C Elich, L Kirchdorfer, JM Köhler, L Schott arXiv preprint arXiv:2311.04698, 2023 | 1 | 2023 |
| Selected Inductive Biases in Neural Networks To Generalize Beyond the Training Domain L Schott University of Tuebingen, 2021 | | 2021 |
| Comparative study of deep learning software frameworks S Bahrampour, N Ramakrishnan, L Schott, M Shah arXiv preprint arXiv:1511.06435, 2015 | | 2015 |
| Diatomic Molecules L Schott, G Wolschin https://www.thphys.uni-heidelberg.de/~wolschin/qms13_5s.pdf, 2013 | | 2013 |
Matthias BethgeTübingen University & Maddox Co-Founder在 bethgelab.org 的电子邮件经过验证
