Christopher J. Anders

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerifierad e-postadress på tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerifierad e-postadress på tu-berlin.de
Sebastian LapuschkinHead of Explainable AI, Fraunhofer Heinrich Hertz InstituteVerifierad e-postadress på hhi.fraunhofer.de
Grégoire MontavonGuest professor, Freie Universität BerlinVerifierad e-postadress på fu-berlin.de
Pan KesselTU BerlinVerifierad e-postadress på tu-berlin.de
Ann-Kathrin DombrowskiTU-BerlinVerifierad e-postadress på tu-berlin.de
Karl JansenVerifierad e-postadress på desy.de
Kim A. Nicoli, Ph.D.PostDoc at University of Bonn, HISKPVerifierad e-postadress på uni-bonn.de
Shinichi NakajimaBIFOLD, Technische Universität BerlinVerifierad e-postadress på tu-berlin.de
David NeumannResearch Associate, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, HHIVerifierad e-postadress på hhi.fraunhofer.de
Lena FunckeJunior Professor, University of BonnVerifierad e-postadress på uni-bonn.de
Leander WeberFraunhofer Heinrich-Hertz-InstitutVerifierad e-postadress på hhi.fraunhofer.de
Paolo StornatiBarcelona Supercomputing CenterVerifierad e-postadress på bsc.es
Maximilian AlberTU BerlinVerifierad e-postadress på tu-berlin.de
Marcel AckermannFreelancer ML ScientistVerifierad e-postadress på stud.tu-darmstadt.de
Frederik PahdeFraunhofer Heinrich Hertz InstituteVerifierad e-postadress på hhi.fraunhofer.de
Maximilian DreyerExplainable AI Group, Fraunhofer Heinrich Hertz InstituteVerifierad e-postadress på hhi.fraunhofer.de
Talmaj MarinčM. Sc.Verifierad e-postadress på toriml.com
Stefan KühnDESYVerifierad e-postadress på desy.de
Sidney BenderTechnical University of BerlinVerifierad e-postadress på tu-berlin.de

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Christopher J. Anders

Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo

Verifierad e-postadress på riken.jp

Machine Learning


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
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	1161	2021
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	386	2019
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	131	2022
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	116	2021
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	116	2020
Fairwashing explanations with off-manifold detergent CJ Anders, P Pasliev, AK Dombrowski, KR Müller, P Kessel Thirty-seventh International Conference on Machine Learning, 2020	103	2020
Towards robust explanations for deep neural networks AK Dombrowski, CJ Anders, KR Müller, P Kessel Pattern Recognition 121, 108194, 2022	76	2022
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	73	2021
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	34*	2019
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 Physical Review D 108 (11), 114501, 2023	20	2023
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	13	2019
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	12	2021
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	9	2022
From hope to safety: Unlearning biases of deep models via gradient penalization in latent space M Dreyer, F Pahde, CJ Anders, W Samek, S Lapuschkin Proceedings of the AAAI Conference on Artificial Intelligence 38 (19), 21046 …, 2024	8	2024
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	8	2024
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	8	2020
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	8	2020
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	6	2020
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence F Pahde, M Dreyer, L Weber, M Weckbecker, CJ Anders, T Wiegand, ... arXiv preprint arXiv:2202.03482, 2022	4	2022
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	2	2023

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