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ORCIDChristopher J. Anders
- > Home > Persons > Christopher J. Anders
Publications
- 2025
[c7]Frederik Pahde, Maximilian Dreyer, Moritz Weckbecker, Leander Weber, Christopher J. Anders, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence. ICLR 2025- [i16]Christopher J. Anders, Kim A. Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli
, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima:
Adaptive Observation Cost Control for Variational Quantum Eigensolvers. CoRR abs/2502.01704 (2025) - [i15]Samuele Pedrielli, Christopher J. Anders, Lena Funcke, Karl Jansen, Kim A. Nicoli, Shinichi Nakajima:
Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers. CoRR abs/2502.02625 (2025) - 2024
- [c6]Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space. AAAI 2024: 21046-21054 - [c5]Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima:
Adaptive Observation Cost Control for Variational Quantum Eigensolvers. ICML 2024: 1557-1578 - [i14]Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima:
Physics-Informed Bayesian Optimization of Variational Quantum Circuits. CoRR abs/2406.06150 (2024) - 2023
- [c4]Sidney Bender, Christopher J. Anders, Pattarawat Chormai, Heike Marxfeld, Jan Herrmann, Grégoire Montavon:
Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation. ICCV (Workshops) 2023: 2599-2607 - [c3]Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima:
Physics-Informed Bayesian Optimization of Variational Quantum Circuits. NeurIPS 2023 - [i13]Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima:
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories. CoRR abs/2302.14082 (2023) - [i12]Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space. CoRR abs/2308.09437 (2023) - [i11]Sidney Bender, Christopher J. Anders, Pattarawat Chormai, Heike Marxfeld, Jan Herrmann, Grégoire Montavon:
Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation. CoRR abs/2310.01011 (2023) - 2022
- [j3]Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin:
Finding and removing Clever Hans: Using explanation methods to debug and improve deep models. Inf. Fusion 77: 261-295 (2022) - [j2]Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller, Pan Kessel:
Towards robust explanations for deep neural networks. Pattern Recognit. 121: 108194 (2022) - [i10]Frederik Pahde, Leander Weber, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin:
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging. CoRR abs/2202.03482 (2022) - 2021
- [j1]Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller:
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications. Proc. IEEE 109(3): 247-278 (2021) - [i9]Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin:
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy. CoRR abs/2106.13200 (2021) - [i8]Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati:
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse. CoRR abs/2111.11303 (2021) - 2020
- [c2]Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel:
Fairwashing explanations with off-manifold detergent. ICML 2020: 314-323 - [i7]Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller:
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond. CoRR abs/2003.07631 (2020) - [i6]Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati:
On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models. CoRR abs/2007.07115 (2020) - [i5]Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel:
Fairwashing Explanations with Off-Manifold Detergent. CoRR abs/2007.09969 (2020) - [i4]Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller, Pan Kessel:
Towards Robust Explanations for Deep Neural Networks. CoRR abs/2012.10425 (2020) - 2019
- [c1]Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel:
Explanations can be manipulated and geometry is to blame. NeurIPS 2019: 13567-13578 - [p1]Christopher J. Anders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
Understanding Patch-Based Learning of Video Data by Explaining Predictions. Explainable AI 2019: 297-309 - [i3]Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel:
Explanations can be manipulated and geometry is to blame. CoRR abs/1906.07983 (2019) - [i2]Christopher J. Anders, Talmaj Marinc, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin:
Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed. CoRR abs/1912.11425 (2019) - 2018
- [i1]Christopher J. Anders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
Understanding Patch-Based Learning by Explaining Predictions. CoRR abs/1806.06926 (2018)
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