Christopher J. Anders

According to our database, Christopher J. Anders authored at least 20 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Debugging learning algorithms: understanding and correcting machine learning models.

Christopher J. Anders

PhD thesis, 2024

Adaptive Observation Cost Control for Variational Quantum Eigensolvers.

Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima

Proceedings of the Forty-first International Conference on Machine Learning, 2024

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space.

Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space.

Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin

CoRR, 2023

Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories.

Kim A. Nicoli, Christopher J. Anders, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima

CoRR, 2023

Physics-Informed Bayesian Optimization of Variational Quantum Circuits.

Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation.

Sidney Bender, Christopher J. Anders, Pattarawat Chormai, Heike Marxfeld, Jan Herrmann, Grégoire Montavon

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Towards robust explanations for deep neural networks.

Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller, Pan Kessel

Pattern Recognit., 2022

Finding and removing Clever Hans: Using explanation methods to debug and improve deep models.

Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin

Inf. Fusion, 2022

PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging.

Frederik Pahde, Leander Weber, Christopher J. Anders, Wojciech Samek, Sebastian Lapuschkin

CoRR, 2022

2021

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications.

Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller

Proc. IEEE, 2021

Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse.

Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati

CoRR, 2021

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy.

Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin

CoRR, 2021

2020

On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models.

Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, Paolo Stornati

CoRR, 2020

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond.

Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller

CoRR, 2020

Fairwashing explanations with off-manifold detergent.

Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, Pan Kessel

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Understanding Patch-Based Learning of Video Data by Explaining Predictions.

Christopher J. Anders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

Proceedings of the Explainable AI: Interpreting, 2019

Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed.

Christopher J. Anders, Talmaj Marinc, David Neumann, Wojciech Samek, Klaus-Robert Müller, Sebastian Lapuschkin

CoRR, 2019

Explanations can be manipulated and geometry is to blame.

Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, Pan Kessel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Understanding Patch-Based Learning by Explaining Predictions.

Christopher J. Anders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

CoRR, 2018