Lukas Schott
Lukas Schott
Bosch Center for AI
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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
Comparative study of deep learning software frameworks
S Bahrampour, N Ramakrishnan, L Schott, M Shah
arXiv preprint arXiv:1511.06435, 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
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
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
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, ...
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
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
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
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
Comparative study of Caffe
S Bahrampour, N Ramakrishnan, L Schott, M Shah
Neon, Theano, and Torch for Deep Learning. arXiv 1511, 2015
Comparative study of deep learning software frameworks. arXiv 2015
S Bahrampour, N Ramakrishnan, L Schott, M Shah
arXiv preprint arXiv:1511.06435 3, 0
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
Challenging Common Assumptions in Multi-task Learning
C Elich, L Kirchdorfer, JM Köhler, L Schott
arXiv preprint arXiv:2311.04698, 2023
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
Selected Inductive Biases in Neural Networks To Generalize Beyond the Training Domain
L Schott
University of Tuebingen, 2021
Diatomic Molecules
L Schott, G Wolschin, 2013
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