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
219*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
150*2018
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
242017
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise
E Rusak, L Schott, R Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ...
arXiv preprint arXiv:2001.06057, 2020
182020
Deep learning on symbolic representations for large-scale heterogeneous time-series event prediction
S Zhang, S Bahrampour, N Ramakrishnan, L Schott, M Shah
NIPS Time Series Workshop, 2016
112016
Comparative study of Caffe
S Bahrampour, N Ramakrishnan, L Schott, M Shah
Neon, Theano, and Torch for Deep Learning, 2016
92016
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
2020
A simple way to make neural networks robust against diverse image corruptions
E Rusak, L Schott, RS Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ...
arXiv preprint arXiv:2001.06057, 2020
2020
Diatomic Molecules
L Schott, G Wolschin
https://www.thphys.uni-heidelberg.de/~wolschin/qms13_5s.pdf, 2013
2013
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Articles 1–9