Layer-wise analysis of deep networks with Gaussian kernels. - Grégoire Montavon; Mikio L. Braun; Klaus-Robert Müller (NIPS, 2010)

Kernel Analysis of Deep Networks. - Grégoire Montavon; Mikio L. Braun; Klaus-Robert Müller (J. Mach. Learn. Res., 2011)

Learning Invariant Representations of Molecules for Atomization Energy Prediction. - Grégoire Montavon; Katja Hansen; Siamac Fazli; Matthias Rupp; Franziska Biegler; Andreas Ziehe; Alexandre Tkatchenko; O. Anatole von Lilienfeld; Klaus-Robert Müller (NIPS, 2012)

Learning Feature Hierarchies with Centered Deep Boltzmann Machines - Grégoire Montavon; Klaus-Robert Müller (CoRR, 2012)

Deep Boltzmann Machines as Feed-Forward Hierarchies. - Grégoire Montavon; Mikio L. Braun; Klaus-Robert Müller (AISTATS, 2012)

Big Learning and Deep Neural Networks. - Grégoire Montavon; Klaus-Robert Müller (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

Better Representations: Invariant, Disentangled and Reusable. - Grégoire Montavon; Klaus-Robert Müller (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

Deep Boltzmann Machines and the Centering Trick. - Grégoire Montavon; Klaus-Robert Müller (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

Identifying Dynamical Systems for Forecasting and Control. - Grégoire Montavon; Klaus-Robert Müller (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

Analyzing Local Structure in Kernel-Based Learning: Explanation, Complexity, and Reliability Assessment. - Grégoire Montavon; Mikio L. Braun; Tammo Krueger; Klaus-Robert Müller (IEEE Signal Process. Mag., 2013)

On layer-wise representations in deep neural networks. - Grégoire Montavon (2013)

Wasserstein Training of Boltzmann Machines. - Grégoire Montavon; Klaus-Robert Müller; Marco Cuturi (CoRR, 2015)

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks. - Sebastian Lapuschkin; Alexander Binder; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (CVPR, 2016)

Identifying Individual Facial Expressions by Deconstructing a Neural Network. - Farhad Arbabzadah; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (GCPR, 2016)

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers. - Alexander Binder; Grégoire Montavon; Sebastian Lapuschkin; Klaus-Robert Müller; Wojciech Samek (ICANN (2), 2016)

Wasserstein Training of Restricted Boltzmann Machines. - Grégoire Montavon; Klaus-Robert Müller; Marco Cuturi (NIPS, 2016)

Explaining Predictions of Non-Linear Classifiers in NLP. - Leila Arras; Franziska Horn; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (Rep4NLP@ACL, 2016)

"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach. - Leila Arras; Franziska Horn; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (CoRR, 2016)

Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation. - Wojciech Samek; Grégoire Montavon; Alexander Binder; Sebastian Lapuschkin; Klaus-Robert Müller (CoRR, 2016)

The LRP Toolbox for Artificial Neural Networks. - Sebastian Lapuschkin; Alexander Binder; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (J. Mach. Learn. Res., 2016)

Explaining Recurrent Neural Network Predictions in Sentiment Analysis. - Leila Arras; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (WASSA@EMNLP, 2017)

Exploring text datasets by visualizing relevant words. - Franziska Horn; Leila Arras; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (CoRR, 2017)

Discovering topics in text datasets by visualizing relevant words. - Franziska Horn; Leila Arras; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (CoRR, 2017)

Explaining nonlinear classification decisions with deep Taylor decomposition. - Grégoire Montavon; Sebastian Lapuschkin; Alexander Binder; Wojciech Samek; Klaus-Robert Müller (Pattern Recognit., 2017)

Evaluating the Visualization of What a Deep Neural Network Has Learned. - Wojciech Samek; Alexander Binder; Grégoire Montavon; Sebastian Lapuschkin; Klaus-Robert Müller (IEEE Trans. Neural Networks Learn. Syst., 2017)

Understanding Patch-Based Learning by Explaining Predictions. - Christopher J. Anders; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller (CoRR, 2018)

Unsupervised Detection and Explanation of Latent-class Contextual Anomalies. - Jacob R. Kauffmann; Grégoire Montavon; Luiz Alberto Lima; Shinichi Nakajima; Klaus-Robert Müller; Nico Görnitz (CoRR, 2018)

Methods for interpreting and understanding deep neural networks. - Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller (Digit. Signal Process., 2018)

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. - Sebastian Lapuschkin; Stephan Wäldchen; Alexander Binder; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller (CoRR, 2019)

From Clustering to Cluster Explanations via Neural Networks. - Jacob R. Kauffmann; Malte Esders; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller (CoRR, 2019)

Explaining and Interpreting LSTMs. - Leila Arras; Jose A. Arjona-Medina; Michael Widrich; Grégoire Montavon; Michael Gillhofer; Klaus-Robert Müller; Sepp Hochreiter; Wojciech Samek (CoRR, 2019)

iNNvestigate Neural Networks! - Maximilian Alber; Sebastian Lapuschkin; Philipp Seegerer; Miriam Hägele; Kristof T. Schütt; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller; Sven Dähne; Pieter-Jan Kindermans (J. Mach. Learn. Res., 2019)

Understanding Patch-Based Learning of Video Data by Explaining Predictions. - Christopher J. Anders; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller (Explainable AI, 2019)

Explaining and Interpreting LSTMs. - Leila Arras; Jose A. Arjona-Medina; Michael Widrich; Grégoire Montavon; Michael Gillhofer; Klaus-Robert Müller; Sepp Hochreiter; Wojciech Samek (Explainable AI, 2019)

Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison. - Grégoire Montavon (Explainable AI, 2019)

Layer-Wise Relevance Propagation: An Overview. - Grégoire Montavon; Alexander Binder; Sebastian Lapuschkin; Wojciech Samek; Klaus-Robert Müller (Explainable AI, 2019)

Explaining the Predictions of Unsupervised Learning Models. - Grégoire Montavon; Jacob R. Kauffmann; Wojciech Samek; Klaus-Robert Müller (xxAI@ICML, 2020)

GraphKKE: graph Kernel Koopman embedding for human microbiome analysis. - Kateryna Melnyk; Stefan Klus; Grégoire Montavon; Tim O. F. Conrad (Appl. Netw. Sci., 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)

XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. - Thomas Schnake; Oliver Eberle; Jonas Lederer; Shinichi Nakajima; Kristof T. Schütt; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2020)

The Clever Hans Effect in Anomaly Detection. - Jacob R. Kauffmann; Lukas Ruff; Grégoire Montavon; Klaus-Robert Müller (CoRR, 2020)

Towards explaining anomalies: A deep Taylor decomposition of one-class models. - Jacob R. Kauffmann; Klaus-Robert Müller; Grégoire Montavon (Pattern Recognit., 2020)

Deep learning for surrogate modelling of 2D mantle convection. - Siddhant Agarwal; Nicola Tosi; Pan Kessel; Doris Breuer; Grégoire Montavon (CoRR, 2021)

Toward Explainable AI for Regression Models. - Simon Letzgus; Patrick Wagner 002; Jonas Lederer; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2021)

A Unifying Review of Deep and Shallow Anomaly Detection. - Lukas Ruff; Jacob R. Kauffmann; Robert A. Vandermeulen; Grégoire Montavon; Wojciech Samek; Marius Kloft; Thomas G. Dietterich; Klaus-Robert Müller (Proc. IEEE, 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)

XAI for Transformers: Better Explanations through Conservative Propagation. - Ameen Ali; Thomas Schnake; Oliver Eberle; Grégoire Montavon; Klaus-Robert Müller; Lior Wolf (ICML, 2022)

Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. - Ping Xiong 002; Thomas Schnake; Grégoire Montavon; Klaus-Robert Müller; Shinichi Nakajima (ICML, 2022)

An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning. - Hassan El-Hajj; Maryam Zamani; Jochen Büttner; Julius Martinetz; Oliver Eberle; Noga Shlomi; Anna Siebold; Grégoire Montavon; Klaus-Robert Müller; Holger Kantz; Matteo Valleriani (Datenbank-Spektrum, 2022)

Building and Interpreting Deep Similarity Models. - Oliver Eberle; Jochen Büttner; Florian Kräutli; Klaus-Robert Müller; Matteo Valleriani; Grégoire Montavon (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Higher-Order Explanations of Graph Neural Networks via Relevant Walks. - Thomas Schnake; Oliver Eberle; Jonas Lederer; Shinichi Nakajima; Kristof T. Schütt; Klaus-Robert Müller; Grégoire Montavon (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective. - Simon Letzgus; Patrick Wagner 002; Jonas Lederer; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (IEEE Signal Process. Mag., 2022)

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. - Alexander Binder; Leander Weber; Sebastian Lapuschkin; Grégoire Montavon; Klaus-Robert Müller; Wojciech Samek (CVPR, 2023)

Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation. - Sidney Bender; Christopher J. Anders; Pattarawat Chormai; Heike Marxfeld; Jan Herrmann; Grégoire Montavon (ICCV (Workshops), 2023)

Relevant Walk Search for Explaining Graph Neural Networks. - Ping Xiong 002; Thomas Schnake; Michael Gastegger; Grégoire Montavon; Klaus-Robert Müller; Shinichi Nakajima (ICML, 2023)

Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI. - Oliver Eberle; Jochen Büttner; Hassan El-Hajj; Grégoire Montavon; Klaus-Robert Müller; Matteo Valleriani (CoRR, 2023)

Learning domain invariant representations by joint Wasserstein distance minimization. - Léo Andéol; Yusei Kawakami; Yuichiro Wada; Takafumi Kanamori; Klaus-Robert Müller; Grégoire Montavon (Neural Networks, 2023)

MambaLRP: Explaining Selective State Space Sequence Models. - Farnoush Rezaei Jafari; Grégoire Montavon; Klaus-Robert Müller; Oliver Eberle (NeurIPS, 2024)

XpertAI: uncovering model strategies for sub-manifolds. - Simon Letzgus; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2024)

The Clever Hans Effect in Unsupervised Learning. - Jacob R. Kauffmann; Jonas Dippel; Lukas Ruff; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2024)

Opportunities and limitations of explaining quantum machine learning. - Elies Gil-Fuster; Jonas R. Naujoks; Grégoire Montavon; Thomas Wiegand 001; Wojciech Samek; Jens Eisert (CoRR, 2024)

Explainable AI for time series via Virtual Inspection Layers. - Johanna Vielhaben; Sebastian Lapuschkin; Grégoire Montavon; Wojciech Samek (Pattern Recognit., 2024)

From Clustering to Cluster Explanations via Neural Networks. - Jacob R. Kauffmann; Malte Esders; Lukas Ruff; Grégoire Montavon; Wojciech Samek; Klaus-Robert Müller (IEEE Trans. Neural Networks Learn. Syst., 2024)

Preemptively pruning Clever-Hans strategies in deep neural networks. - Lorenz Linhardt; Klaus-Robert Müller; Grégoire Montavon (Inf. Fusion, 2024)

Code and Data for Historical Insights from Sacrobosco Tables Project. - Oliver Eberle; Jochen Büttner; Hassan El-Hajj; Grégoire Montavon; Klaus-Robert Müller; Matteo Valleriani (Zenodo, 2024)

Code and Data for "Historical Insights from Sacrobosco Tables" Project. - Oliver Eberle; Jochen Büttner; Hassan El-Hajj; Grégoire Montavon; Klaus-Robert Müller; Matteo Valleriani (Zenodo, 2024)

Explainable AI Reveals Clever Hans Effects in Unsupervised Learning Models: Code. - Jacob R. Kauffmann; Jonas Dippel; Lukas Ruff; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (Zenodo, 2024)

Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces. - Pattarawat Chormai; Jan Herrmann; Klaus-Robert Müller; Grégoire Montavon (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Towards symbolic XAI - explanation through human understandable logical relationships between features. - Thomas Schnake; Farnoush Rezaei Jafari; Jonas Lederer; Ping Xiong 002; Shinichi Nakajima; Stefan Gugler; Grégoire Montavon; Klaus-Robert Müller (Inf. Fusion, 2025)

Explainable AI reveals Clever Hans effects in unsupervised learning models. - Jacob R. Kauffmann; Jonas Dippel; Lukas Ruff; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (Nat. Mac. Intell., 2025)

Explaining predictive uncertainty by exposing second-order effects. - Florian Bley; Sebastian Lapuschkin; Wojciech Samek; Grégoire Montavon (Pattern Recognit., 2025)

Uncovering the Structure of Explanation Quality with Spectral Analysis. - Johannes Maeß; Grégoire Montavon; Shinichi Nakajima; Klaus-Robert Müller; Thomas Schnake (CoRR, 2025)

Wasserstein Distances Made Explainable: Insights into Dataset Shifts and Transport Phenomena. - Philip Naumann; Jacob R. Kauffmann; Grégoire Montavon (CoRR, 2025)

Towards Desiderata-Driven Design of Visual Counterfactual Explainers. - Sidney Bender; Jan Herrmann; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2025)

MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification. - David Jacob Drexlin; Jonas Dippel; Julius Hense; Niklas Prenißl; Grégoire Montavon; Frederick Klauschen; Klaus-Robert Müller (CoRR, 2025)

Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures. - Florian Bley; Jacob R. Kauffmann; Simon León Krug; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2025)