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Lukas Schott
Lukas Schott
PhD Student International Max Planck Research School for Intelligent Systems
Verified email at bethgelab.org
Title
Cited by
Cited by
Year
Comparative study of deep learning software frameworks
S Bahrampour, N Ramakrishnan, L Schott, M Shah
arXiv preprint arXiv:1511.06435, 2015
317*2015
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
2982018
A simple way to make neural networks robust against diverse image corruptions
E Rusak, L Schott, RS Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ...
European Conference on Computer Vision, 53-69, 2020
1072020
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
372020
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, ...
362020
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
362017
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
182016
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
122021
Comparative study of Caffe
S Bahrampour, N Ramakrishnan, L Schott, M Shah
Neon, Theano, and Torch for Deep Learning. arXiv 1511, 2015
72015
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
42018
Score-based generative classifiers
RS Zimmermann, L Schott, Y Song, BA Dunn, DA Klindt
arXiv preprint arXiv:2110.00473, 2021
32021
Selected Inductive Biases in Neural Networks To Generalize Beyond the Training Domain
L Schott
University of Tuebingen, 2021
2021
Diatomic Molecules
L Schott, G Wolschin
https://www.thphys.uni-heidelberg.de/~wolschin/qms13_5s.pdf, 2013
2013
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Articles 1–13