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Christopher J. Anders
Christopher J. Anders
Verified email at tu-berlin.de
Title
Cited by
Cited by
Year
Explaining deep neural networks and beyond: A review of methods and applications
W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller
Proceedings of the IEEE 109 (3), 247-278, 2021
7762021
Explanations can be manipulated and geometry is to blame
AK Dombrowski, M Alber, C Anders, M Ackermann, KR Müller, P Kessel
Advances in neural information processing systems 32, 2019
3032019
Toward interpretable machine learning: Transparent deep neural networks and beyond
W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller
arXiv preprint arXiv:2003.07631 2, 2020
1082020
Finding and removing Clever Hans: using explanation methods to debug and improve deep models
CJ Anders, L Weber, D Neumann, W Samek, KR Müller, S Lapuschkin
Information Fusion 77, 261-295, 2022
932022
Estimation of thermodynamic observables in lattice field theories with deep generative models
KA Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, P Kessel, ...
Physical review letters 126 (3), 032001, 2021
932021
Fairwashing explanations with off-manifold detergent
CJ Anders, P Pasliev, AK Dombrowski, KR Müller, P Kessel
Thirty-seventh International Conference on Machine Learning, 2020
882020
Software for dataset-wide XAI: from local explanations to global insights with Zennit, CoRelAy, and ViRelAy
CJ Anders, D Neumann, W Samek, KR Müller, S Lapuschkin
arXiv preprint arXiv:2106.13200, 2021
582021
Towards robust explanations for deep neural networks
AK Dombrowski, CJ Anders, KR Müller, P Kessel
Pattern Recognition 121, 108194, 2022
572022
Understanding patch-based learning of video data by explaining predictions
CJ Anders, G Montavon, W Samek, KR Müller
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 297-309, 2019
28*2019
Analyzing imagenet with spectral relevance analysis: Towards imagenet un-hans’ ed
CJ Anders, T Marinc, D Neumann, W Samek, KR Müller, S Lapuschkin
arXiv preprint arXiv:1912.11425 2 (3), 2019
132019
Toward interpretable machine learning: Transparent deep neural networks and beyond. arXiv 2020
W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller
arXiv preprint arXiv:2003.07631, 2020
82020
Toward interpretable machine learning: Transparent deep neural networks and beyond. arXiv
W Samek, G Montavon, S Lapuschkin, CJ Anders, KR Müller
arXiv preprint arXiv:2003.07631, 2020
82020
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
KA Nicoli, CJ Anders, T Hartung, K Jansen, P Kessel, S Nakajima
arXiv preprint arXiv:2302.14082, 2023
72023
XAI for analyzing and unlearning spurious correlations in ImageNet
CJ Anders, D Neumann, T Marinc, W Samek, KR Müller, S Lapuschkin
Proceedings of the 2020 ICML Workshop on Extending Explainable AI Beyond …, 2020
62020
Machine learning of thermodynamic observables in the presence of mode collapse
KA Nicoli, C Anders, L Funcke, T Hartung, K Jansen, P Kessel, ...
arXiv preprint arXiv:2111.11303, 2021
52021
Physics-informed bayesian optimization of variational quantum circuits
K Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, S Kühn, KR Müller, ...
Advances in Neural Information Processing Systems 36, 2024
22024
PatClArC: Using pattern concept activation vectors for noise-robust model debugging
F Pahde, L Weber, CJ Anders, W Samek, S Lapuschkin
arXiv preprint arXiv:2202.03482, 2022
22022
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space
M Dreyer, F Pahde, CJ Anders, W Samek, S Lapuschkin
arXiv preprint arXiv:2308.09437, 2023
2023
Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation
S Bender, CJ Anders, P Chormai, HA Marxfeld, J Herrmann, G Montavon
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
2023
NeuLat: a toolbox for neural samplers in lattice field theories
KA Nicoli, CJ Anders, L Funcke, K Jansen, S Nakajima𝑑, P Kesselℎ
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