Christopher J. Anders

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All published works

Action	Title	Year	Authors
+ PDF Chat	Physics-Informed Bayesian Optimization of Variational Quantum Circuits	2024	Kim A. Nicoli Christopher J. Anders Lena Funcke Tobias Hartung Karl Jansen Stefan Kühn Klaus‐Robert Müller Paolo Stornati Pan Kessel Shinichi Nakajima
+ PDF Chat	From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space	2024	Maximilian Dreyer Frederik Pahde Christopher J. Anders Wojciech Samek Sebastian Lapuschkin
+ PDF Chat	Detecting and mitigating mode-collapse for flow-based sampling of lattice field theories	2023	Kim A. Nicoli Christopher J. Anders Tobias Hartung Karl Jansen Pan Kessel Shinichi Nakajima
+ PDF Chat	Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation	2023	Sidney Bender Christopher J. Anders Pattarawat Chormai Heike Marxfeld Jan Herrmann Grégoire Montavon
+	Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories	2023	Kim A. Nicoli Christopher J. Anders Tobias Hartung Karl Jansen Pan Kessel Shinichi Nakajima
+	From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space	2023	Maximilian Dreyer Frederik Pahde Christopher J. Anders Wojciech Samek Sebastian Lapuschkin
+	Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation	2023	Sidney Bender Christopher J. Anders Pattarawatt Chormai Heike Marxfeld Jan Herrmann Grégoire Montavon
+ PDF Chat	Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse	2022	Kim A. Nicoli Christopher J. Anders Lena Funcke Tobias Hartung Karl Jansen Pan Kessel Shinichi Nakajima Paolo Stornati
+	PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging	2022	Frederik Pahde Leander Weber Christopher J. Anders Wojciech Samek Sebastian Lapuschkin
+ PDF Chat	Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse	2021	Kim A. Nicoli Christopher J. Anders Lena Funcke Tobias Hartung Karl Jansen Pan Kessel Shinichi Nakajima Paolo Stornati
+	Finding and removing Clever Hans: Using explanation methods to debug and improve deep models	2021	Christopher J. Anders Leander Weber David Neumann Wojciech Samek Klaus‐Robert Müller Sebastian Lapuschkin
+	Towards robust explanations for deep neural networks	2021	Ann-Kathrin Dombrowski Christopher J. Anders Klaus‐Robert Müller Pan Kessel
+	Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy.	2021	Christopher J. Anders David Neumann Wojciech Samek Klaus‐Robert Müller Sebastian Lapuschkin
+ PDF Chat	Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications	2021	Wojciech Samek Grégoire Montavon Sebastian Lapuschkin Christopher J. Anders Klaus‐Robert Müller
+ PDF Chat	Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models	2021	Kim A. Nicoli Christopher J. Anders Lena Funcke Tobias Hartung Karl Jansen Pan Kessel Shinichi Nakajima Paolo Stornati
+	Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy	2021	Christopher J. Anders David Neumann Wojciech Samek Klaus‐Robert Müller Sebastian Lapuschkin
+	Fairwashing Explanations with Off-Manifold Detergent	2020	Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski Klaus‐Robert Müller Pan Kessel
+	Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond	2020	Wojciech Samek Grégoire Montavon Sebastian Lapuschkin Christopher J. Anders Klaus‐Robert Müller
+	Fairwashing Explanations with Off-Manifold Detergent	2020	Christopher J. Anders Plamen Plasiliev Ann-Kathrin Dombrowski Klaus‐Robert Mueller Pan Kessel
+	Towards Robust Explanations for Deep Neural Networks	2020	Ann-Kathrin Dombrowski Christopher J. Anders Klaus‐Robert Müller Pan Kessel
+	Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed.	2019	Christopher J. Anders Talmaj Marinč David Neumann Wojciech Samek Klaus‐Robert Müller Sebastian Lapuschkin
+	Explanations can be manipulated and geometry is to blame	2019	Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders Marcel R. Ackermann Klaus‐Robert Müller Pan Kessel
+	Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models	2019	Christopher J. Anders Leander Weber David Neumann Wojciech Samek Klaus‐Robert Müller Sebastian Lapuschkin
+	Understanding Patch-Based Learning by Explaining Predictions	2018	Christopher J. Anders Grégoire Montavon Wojciech Samek Klaus‐Robert Müller
+	Understanding Patch-Based Learning by Explaining Predictions	2018	Christopher J. Anders Grégoire Montavon Wojciech Samek Klaus‐Robert Müller

Common Coauthors

Coauthor	Papers Together
Klaus‐Robert Müller	14
Wojciech Samek	12
Pan Kessel	11
Sebastian Lapuschkin	10
Karl Jansen	6
Shinichi Nakajima	6
Kim A. Nicoli	6
Grégoire Montavon	6
Tobias Hartung	6
Ann-Kathrin Dombrowski	5
David Neumann	5
Paolo Stornati	4
Lena Funcke	4
Leander Weber	3
Frederik Pahde	3
Heike Marxfeld	2
Sidney Bender	2
Jan Herrmann	2
Marcel R. Ackermann	1
Klaus‐Robert Mueller	1
Plamen Pasliev	1
Maximilian Dreyer	1
Pattarawat Chormai	1
Plamen Plasiliev	1
Pattarawatt Chormai	1
Maximilian Alber	1
Talmaj Marinč	1
Maximilian Dreyer	1
Stefan Kühn	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Unmasking Clever Hans predictors and assessing what machines really learn	2019	Sebastian Lapuschkin Stephan Wäldchen Alexander Binder Grégoire Montavon Wojciech Samek Klaus‐Robert Müller	9
+	Explaining nonlinear classification decisions with deep Taylor decomposition	2016	Grégoire Montavon Sebastian Lapuschkin Alexander Binder Wojciech Samek Klaus‐Robert Müller	8
+ PDF Chat	Evaluating the Visualization of What a Deep Neural Network Has Learned	2016	Wojciech Samek Alexander Binder Grégoire Montavon Sebastian Lapuschkin Klaus‐Robert Müller	8
+	Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)	2017	Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler Fernanda Viégas Rory Sayres	7
+ PDF Chat	Interpretable Explanations of Black Boxes by Meaningful Perturbation	2017	Ruth Fong Andrea Vedaldi	7
+	Methods for interpreting and understanding deep neural networks	2017	Grégoire Montavon Wojciech Samek Klaus‐Robert Müller	7
+	SmoothGrad: removing noise by adding noise	2017	Daniel Smilkov Nikhil Thorat Been Kim Fernanda Viégas Martin Wattenberg	7
+	Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps	2013	Karen Simonyan Andrea Vedaldi Andrew Zisserman	7
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	7
+ PDF Chat	ImageNet Large Scale Visual Recognition Challenge	2015	Olga Russakovsky Jia Deng Hao Su Jonathan Krause Sanjeev Satheesh Sean Ma Zhiheng Huang Andrej Karpathy Aditya Khosla Michael S. Bernstein	6
+ PDF Chat	Analyzing Classifiers: Fisher Vectors and Deep Neural Networks	2016	Sebastian Lapuschkin Alexander Binder Grégoire Montavon Klaus‐Robert Müller Wojciech Samek	6
+	Learning Important Features Through Propagating Activation Differences	2017	Avanti Shrikumar Peyton Greenside Anshul Kundaje	6
+ PDF Chat	Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization	2017	Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra	6
+	Axiomatic Attribution for Deep Networks	2017	Mukund Sundararajan Ankur Taly Qiqi Yan	6
+	Striving for Simplicity: The All Convolutional Net	2014	Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller	6
+ PDF Chat	Quantum-chemical insights from deep tensor neural networks	2017	Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela K. Müller Alexandre Tkatchenko	6
+ PDF Chat	Towards Best Practice in Explaining Neural Network Decisions with LRP	2020	Maximilian Kohlbrenner Alexander Bauer Shinichi Nakajima Alexander Binder Wojciech Samek Sebastian Lapuschkin	5
+	How to Explain Individual Classification Decisions	2009	David Baehrens Timon Schroeter Stefan Harmeling Motoaki Kawanabe Katja Hansen Klaus‐Robert Müller	5
+	Learning how to explain neural networks: PatternNet and PatternAttribution	2017	Pieter Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller Dumitru Erhan Been Kim Sven Dähne	5
+ PDF Chat	A Unifying Review of Deep and Shallow Anomaly Detection	2021	Lukas Ruff Jacob R. Kauffmann Robert A. Vandermeulen Grégoire Montavon Wojciech Samek Marius Kloft Thomas G. Dietterich Klaus‐Robert Müller	5
+ PDF Chat	Resolving challenges in deep learning-based analyses of histopathological images using explanation methods	2020	Miriam Hägele Philipp Seegerer Sebastian Lapuschkin Michael Bockmayr Wojciech Samek Frederick Klauschen Klaus‐Robert Müller Alexander Binder	5
+	Very Deep Convolutional Networks for Large-Scale Image Recognition	2014	Karen Simonyan Andrew Zisserman	5
+	Towards explaining anomalies: A deep Taylor decomposition of one-class models	2020	Jacob R. Kauffmann Klaus‐Robert Müller Grégoire Montavon	4
+	Very Deep Convolutional Networks for Large-Scale Image Recognition	2014	Karen Simonyan Andrew Zisserman	4
+ PDF Chat	Building and Interpreting Deep Similarity Models	2020	Oliver Eberle Jochen Büttner Florian Kräutli Klaus‐Robert Müller Matteo Valleriani Grégoire Montavon	4
+	A Benchmark for Interpretability Methods in Deep Neural Networks	2018	Sara Hooker Dumitru Erhan Pieter-Jan Kindermans Been Kim	4
+ PDF Chat	Towards Evaluating the Robustness of Neural Networks	2017	Nicholas Carlini David Wagner	4
+	iNNvestigate Neural Networks	2019	Maximilian Alber Sebastian Lapuschkin Philipp Seegerer Miriam Hägele Kristof T. Schütt Grégoire Montavon Wojciech Samek K. Müller Sven Dähne Pieter Jan Kindermans	4
+ PDF Chat	From Clustering to Cluster Explanations via Neural Networks	2022	Jacob R. Kauffmann Malte Esders Lukas Ruff Grégoire Montavon Wojciech Samek Klaus‐Robert Müller	4
+ PDF Chat	"What is relevant in a text document?": An interpretable machine learning approach	2017	Leila Arras Franziska Horn Grégoire Montavon Klaus‐Robert Müller Wojciech Samek	4
+	Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations	2017	Andrew Slavin Ross Michael C. Hughes Finale Doshi‐Velez	4
+ PDF Chat	Equivariant Flow-Based Sampling for Lattice Gauge Theory	2020	Gurtej Kanwar Michael S. Albergo Denis Boyda K. Cranmer Daniel C. Hackett Sébastien Racanière Danilo Jimenez Rezende Phiala E. Shanahan	3
+	Fairwashing Explanations with Off-Manifold Detergent	2020	Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski Klaus‐Robert Müller Pan Kessel	3
+ PDF Chat	Making deep neural networks right for the right scientific reasons by interacting with their explanations	2020	Patrick Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Franziska Herbert Xiaoting Shao Hans-Georg Luigs Anne‐Katrin Mahlein Kristian Kersting	3
+ PDF Chat	Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning	2019	Frank Noé Simon Olsson Jonas Köhler Hao Wu	3
+ PDF Chat	Understanding Deep Networks via Extremal Perturbations and Smooth Masks	2019	Ruth Fong Mandela Patrick Andrea Vedaldi	3
+ PDF Chat	Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation	2014	Charlotte Soneson Sarah Gerster Mauro Delorenzi	3
+ PDF Chat	Learning Deep Features for Discriminative Localization	2016	Bolei Zhou Aditya Khosla Àgata Lapedriza Aude Oliva Antonio Torralba	3
+	Explaining Recurrent Neural Network Predictions in Sentiment Analysis	2017	Leila Arras Grégoire Montavon Klaus‐Robert Müller Wojciech Samek	3
+ PDF Chat	Asymptotically unbiased estimation of physical observables with neural samplers	2020	Kim A. Nicoli Shinichi Nakajima Nils Strodthoff Wojciech Samek Klaus‐Robert Müller Pan Kessel	3
+ PDF Chat	Classifying and segmenting microscopy images with deep multiple instance learning	2016	Oren Kraus Jimmy Ba Brendan J. Frey	3
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee	3
+	Real Time Image Saliency for Black Box Classifiers	2017	Piotr Dabkowski Yarin Gal	3
+	On the interpretation of weight vectors of linear models in multivariate neuroimaging	2013	Stefan Haufe Frank C. Meinecke Kai Görgen Sven Dähne John–Dylan Haynes Benjamin Blankertz Felix Bießmann	3
+	Explanations can be manipulated and geometry is to blame	2019	Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders Marcel R. Ackermann Klaus‐Robert Müller Pan Kessel	3
+ PDF Chat	Interpretable deep neural networks for single-trial EEG classification	2016	Irene Sturm Sebastian Lapuschkin Wojciech Samek Klaus‐Robert Müller	3
+	Graying the black box: Understanding DQNs	2016	Tom Zahavy Nir Ben Zrihem Shie Mannor	3
+ PDF Chat	A Survey of Methods for Explaining Black Box Models	2018	Riccardo Guidotti Anna Monreale Salvatore Ruggieri Franco Turini Fosca Giannotti Dino Pedreschi	3
+ PDF Chat	European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”	2017	Bryce Goodman Seth Flaxman	3
+ PDF Chat	Top-Down Neural Attention by Excitation Backprop	2017	Jianming Zhang Sarah Adel Bargal Zhe Lin Jonathan Brandt Xiaohui Shen Stan Sclaroff	3