Lukas Schott

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	All	Since 2017
Citations	875	838
h-index	8	8
i10-index	8	8

220

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165

201620172018201920202021202229 68 70 121 194 215 168

Co-authors

Matthias BethgeTübingen University & EPFL Visiting ProfessorVerified email at bethgelab.org
Wieland BrendelResearch Group Leader, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Soheil BahrampourGoogleVerified email at google.com
Jonas RauberUniversity of Tübingen & Max Planck Research School for Intelligent SystemsVerified email at uni-tuebingen.de
Roland Simon ZimmermannUniversity of TübingenVerified email at uni-tuebingen.de
Evgenia RusakM.Sc.Verified email at uni-tuebingen.de
Julian BitterwolfPhD Student, University of TübingenVerified email at uni-tuebingen.de
David KlindtMeta CTRL LabsVerified email at studium.fernuni-hagen.de
Dylan M PaitonInstitute for Theoretical Physics, University of TübingenVerified email at bethgelab.org
Ivan UstyuzhaninovPhD student, University of TübingenVerified email at bethgelab.org
Steffen WolfMRC Laboratory of Molecular Biology, CambridgeVerified email at mrc-lmb.cam.ac.uk
Ullrich KötheAdjunct Professor of Computer Science, University of HeidelbergVerified email at iwr.uni-heidelberg.de
Fred A. HamprechtProfessor, Heidelberg UniversityVerified email at iwr.uni-heidelberg.de
Shengdong ZhangVerified email at sfu.ca

Lukas Schott

PhD Student International Max Planck Research School for Intelligent Systems

Verified email at bethgelab.org

Robust Vision Adversarial Examples Artificial Intelligence Machine Learning


Title Sort by citations Sort by year Sort by 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	298	2018
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	107	2020
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	37	2020
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, ...	36	2020
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	36	2017
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	18	2016
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	12	2021
Comparative study of Caffe S Bahrampour, N Ramakrishnan, L Schott, M Shah Neon, Theano, and Torch for Deep Learning. arXiv 1511, 2015	7	2015
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	4	2018
Score-based generative classifiers RS Zimmermann, L Schott, Y Song, BA Dunn, DA Klindt arXiv preprint arXiv:2110.00473, 2021	3	2021
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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