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ORCIDGrégoire Montavon
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- affiliation: TU Berlin, Institute of Software Engineering and Theoretical Computer Science, Germany
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2020 – today
- 2024
[j20]Lorenz Linhardt
, Klaus-Robert Müller, Grégoire Montavon:
Preemptively pruning Clever-Hans strategies in deep neural networks. Inf. Fusion 103: 102094 (2024)- [j19]Pattarawat Chormai, Jan Herrmann, Klaus-Robert Müller, Grégoire Montavon:
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 46(11): 7283-7299 (2024) - [j18]Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek:
Explainable AI for time series via Virtual Inspection Layers. Pattern Recognit. 150: 110309 (2024) - [j17]Jacob R. Kauffmann, Malte Esders, Lukas Ruff, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
From Clustering to Cluster Explanations via Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 35(2): 1926-1940 (2024) - [e3]Luca Longo, Weiru Liu, Grégoire Montavon:
Joint Proceedings of the xAI 2024 Late-breaking Work, Demos and Doctoral Consortium co-located with the 2nd World Conference on eXplainable Artificial Intelligence (xAI-2024), Valletta, Malta, July 17-19, 2024. CEUR Workshop Proceedings 3793, CEUR-WS.org 2024 [contents] - [d2]Oliver Eberle, Jochen Büttner, Hassan El-Hajj, Grégoire Montavon, Klaus-Robert Müller, Matteo Valleriani:
Code and Data for Historical Insights from Sacrobosco Tables Project. Version 1. Zenodo, 2024 [all versions]
- [d1]Oliver Eberle, Jochen Büttner, Hassan El-Hajj, Grégoire Montavon, Klaus-Robert Müller, Matteo Valleriani:
Code and Data for "Historical Insights from Sacrobosco Tables" Project. Version 2. Zenodo, 2024 [all versions] - [i42]Florian Bley, Sebastian Lapuschkin, Wojciech Samek, Grégoire Montavon:
Explaining Predictive Uncertainty by Exposing Second-Order Effects. CoRR abs/2401.17441 (2024) - [i41]Simon Letzgus, Klaus-Robert Müller, Grégoire Montavon:
XpertAI: uncovering model strategies for sub-manifolds. CoRR abs/2403.07486 (2024) - [i40]Farnoush Rezaei Jafari, Grégoire Montavon, Klaus-Robert Müller, Oliver Eberle:
MambaLRP: Explaining Selective State Space Sequence Models. CoRR abs/2406.07592 (2024) - [i39]Jacob R. Kauffmann, Jonas Dippel, Lukas Ruff, Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon:
The Clever Hans Effect in Unsupervised Learning. CoRR abs/2408.08041 (2024) - [i38]Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer, Ping Xiong, Shinichi Nakajima, Stefan Gugler, Grégoire Montavon, Klaus-Robert Müller:
Towards Symbolic XAI - Explanation Through Human Understandable Logical Relationships Between Features. CoRR abs/2408.17198 (2024) - 2023
- [j16]Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon:
Learning domain invariant representations by joint Wasserstein distance minimization. Neural Networks 167: 233-243 (2023) - [c15]Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. CVPR 2023: 16143-16152 - [c14]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 - [c13]Ping Xiong, Thomas Schnake, Michael Gastegger, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima:
Relevant Walk Search for Explaining Graph Neural Networks. ICML 2023: 38301-38324 - [i37]Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon, Wojciech Samek:
Explainable AI for Time Series via Virtual Inspection Layers. CoRR abs/2303.06365 (2023) - [i36]Lorenz Linhardt, Klaus-Robert Müller, Grégoire Montavon:
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks. CoRR abs/2304.05727 (2023) - [i35]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) - [i34]Oliver Eberle, Jochen Büttner, Hassan El-Hajj, Grégoire Montavon, Klaus-Robert Müller, Matteo Valleriani:
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI. CoRR abs/2310.09091 (2023) - 2022
- [j15]Hassan El-Hajj, Maryam Zamani, Jochen Büttner, Julius Martinetz, Oliver Eberle, Noga Shlomi, Anna Siebold, Grégoire Montavon, Klaus-Robert Müller, Holger Kantz, Matteo Valleriani:
An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning. Datenbank-Spektrum 22(2): 153-162 (2022) - [j14]Oliver Eberle, Jochen Büttner, Florian Kräutli, Klaus-Robert Müller, Matteo Valleriani, Grégoire Montavon:
Building and Interpreting Deep Similarity Models. IEEE Trans. Pattern Anal. Mach. Intell. 44(3): 1149-1161 (2022) - [j13]Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon:
Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7581-7596 (2022) - [j12]Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon:
Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective. IEEE Signal Process. Mag. 39(4): 40-58 (2022) - [c12]Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf:
XAI for Transformers: Better Explanations through Conservative Propagation. ICML 2022: 435-451 - [c11]Ping Xiong, Thomas Schnake, Grégoire Montavon, Klaus-Robert Müller, Shinichi Nakajima:
Efficient Computation of Higher-Order Subgraph Attribution via Message Passing. ICML 2022: 24478-24495 - [i33]Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf:
XAI for Transformers: Better Explanations through Conservative Propagation. CoRR abs/2202.07304 (2022) - [i32]Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations. CoRR abs/2211.12486 (2022) - [i31]Pattarawat Chormai, Jan Herrmann, Klaus-Robert Müller, Grégoire Montavon:
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces. CoRR abs/2212.14855 (2022) - 2021
- [j11]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) - [j10]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. Proc. IEEE 109(5): 756-795 (2021) - [i30]Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon:
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization. CoRR abs/2106.04923 (2021) - [i29]Siddhant Agarwal, Nicola Tosi, Pan Kessel, Doris Breuer, Grégoire Montavon:
Deep learning for surrogate modelling of 2D mantle convection. CoRR abs/2108.10105 (2021) - [i28]Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, Grégoire Montavon:
Toward Explainable AI for Regression Models. CoRR abs/2112.11407 (2021) - 2020
- [j9]Kateryna Melnyk, Stefan Klus, Grégoire Montavon, Tim O. F. Conrad:
GraphKKE: graph Kernel Koopman embedding for human microbiome analysis. Appl. Netw. Sci. 5(1): 96 (2020) - [j8]Jacob R. Kauffmann, Klaus-Robert Müller, Grégoire Montavon:
Towards explaining anomalies: A deep Taylor decomposition of one-class models. Pattern Recognit. 101: 107198 (2020) - [c10]Grégoire Montavon, Jacob R. Kauffmann, Wojciech Samek, Klaus-Robert Müller:
Explaining the Predictions of Unsupervised Learning Models. xxAI@ICML 2020: 117-138 - [i27]Oliver Eberle, Jochen Büttner, Florian Kräutli, Klaus-Robert Müller, Matteo Valleriani, Grégoire Montavon:
Building and Interpreting Deep Similarity Models. CoRR abs/2003.05431 (2020) - [i26]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) - [i25]Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon:
XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks. CoRR abs/2006.03589 (2020) - [i24]Jacob R. Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller:
The Clever Hans Effect in Anomaly Detection. CoRR abs/2006.10609 (2020) - [i23]Kateryna Melnyk, Stefan Klus, Grégoire Montavon, Tim O. F. Conrad:
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis. CoRR abs/2008.05903 (2020) - [i22]Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller:
A Unifying Review of Deep and Shallow Anomaly Detection. CoRR abs/2009.11732 (2020)
2010 – 2019
- 2019
- [j7]Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans:
iNNvestigate Neural Networks! J. Mach. Learn. Res. 20: 93:1-93:8 (2019) - [p8]Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Wojciech Samek, Klaus-Robert Müller:
Layer-Wise Relevance Propagation: An Overview. Explainable AI 2019: 193-209 - [p7]Leila Arras, Jose A. Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, Wojciech Samek:
Explaining and Interpreting LSTMs. Explainable AI 2019: 211-238 - [p6]Grégoire Montavon:
Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison. Explainable AI 2019: 253-265 - [p5]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 - [e2]Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller:
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Lecture Notes in Computer Science 11700, Springer 2019, ISBN 978-3-030-28953-9 [contents] - [i21]Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn. CoRR abs/1902.10178 (2019) - [i20]Jacob R. Kauffmann, Malte Esders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
From Clustering to Cluster Explanations via Neural Networks. CoRR abs/1906.07633 (2019) - [i19]Leila Arras, Jose A. Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, Wojciech Samek:
Explaining and Interpreting LSTMs. CoRR abs/1909.12114 (2019) - 2018
- [j6]Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73: 1-15 (2018) - [i18]Jacob R. Kauffmann, Klaus-Robert Müller, Grégoire Montavon:
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models. CoRR abs/1805.06230 (2018) - [i17]Christopher J. Anders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
Understanding Patch-Based Learning by Explaining Predictions. CoRR abs/1806.06926 (2018) - [i16]Jacob R. Kauffmann, Grégoire Montavon, Luiz Alberto Lima, Shinichi Nakajima, Klaus-Robert Müller, Nico Görnitz:
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies. CoRR abs/1806.11326 (2018) - [i15]Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans:
iNNvestigate neural networks! CoRR abs/1808.04260 (2018) - 2017
- [j5]Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, Klaus-Robert Müller:
Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 65: 211-222 (2017) - [j4]Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller:
Evaluating the Visualization of What a Deep Neural Network Has Learned. IEEE Trans. Neural Networks Learn. Syst. 28(11): 2660-2673 (2017) - [c9]Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Explaining Recurrent Neural Network Predictions in Sentiment Analysis. WASSA@EMNLP 2017: 159-168 - [i14]Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Explaining Recurrent Neural Network Predictions in Sentiment Analysis. CoRR abs/1706.07206 (2017) - [i13]Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller:
Methods for Interpreting and Understanding Deep Neural Networks. CoRR abs/1706.07979 (2017) - [i12]Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Exploring text datasets by visualizing relevant words. CoRR abs/1707.05261 (2017) - [i11]Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Discovering topics in text datasets by visualizing relevant words. CoRR abs/1707.06100 (2017) - 2016
- [j3]Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
The LRP Toolbox for Artificial Neural Networks. J. Mach. Learn. Res. 17: 114:1-114:5 (2016) - [c8]Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks. CVPR 2016: 2912-2920 - [c7]Farhad Arbabzadah, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Identifying Individual Facial Expressions by Deconstructing a Neural Network. GCPR 2016: 344-354 - [c6]Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, Wojciech Samek:
Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers. ICANN (2) 2016: 63-71 - [c5]Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi:
Wasserstein Training of Restricted Boltzmann Machines. NIPS 2016: 3711-3719 - [c4]Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Explaining Predictions of Non-Linear Classifiers in NLP. Rep4NLP@ACL 2016: 1-7 - [i10]Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller, Wojciech Samek:
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers. CoRR abs/1604.00825 (2016) - [i9]Farhad Arbabzadah, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Identifying individual facial expressions by deconstructing a neural network. CoRR abs/1606.07285 (2016) - [i8]Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Explaining Predictions of Non-Linear Classifiers in NLP. CoRR abs/1606.07298 (2016) - [i7]Wojciech Samek, Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Klaus-Robert Müller:
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation. CoRR abs/1611.08191 (2016) - [i6]Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach. CoRR abs/1612.07843 (2016) - 2015
- [i5]Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi:
Wasserstein Training of Boltzmann Machines. CoRR abs/1507.01972 (2015) - [i4]Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller:
Evaluating the visualization of what a Deep Neural Network has learned. CoRR abs/1509.06321 (2015) - [i3]Sebastian Bach, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek:
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks. CoRR abs/1512.00172 (2015) - [i2]Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, Klaus-Robert Müller:
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition. CoRR abs/1512.02479 (2015) - 2013
- [b1]Grégoire Montavon:
On layer-wise representations in deep neural networks. Berlin Institute of Technology, 2013 - [j2]Grégoire Montavon, Mikio L. Braun, Tammo Krueger, Klaus-Robert Müller:
Analyzing Local Structure in Kernel-Based Learning: Explanation, Complexity, and Reliability Assessment. IEEE Signal Process. Mag. 30(4): 62-74 (2013) - 2012
- [c3]Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Klaus-Robert Müller:
Learning Invariant Representations of Molecules for Atomization Energy Prediction. NIPS 2012: 449-457 - [c2]Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller:
Deep Boltzmann Machines as Feed-Forward Hierarchies. AISTATS 2012: 798-804 - [p4]Grégoire Montavon, Klaus-Robert Müller:
Big Learning and Deep Neural Networks. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 419-420 - [p3]Grégoire Montavon, Klaus-Robert Müller:
Better Representations: Invariant, Disentangled and Reusable. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 559-560 - [p2]Grégoire Montavon, Klaus-Robert Müller:
Deep Boltzmann Machines and the Centering Trick. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 621-637 - [p1]Grégoire Montavon, Klaus-Robert Müller:
Identifying Dynamical Systems for Forecasting and Control. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 657-658 - [e1]Grégoire Montavon, Genevieve B. Orr, Klaus-Robert Müller:
Neural Networks: Tricks of the Trade - Second Edition. Lecture Notes in Computer Science 7700, Springer 2012, ISBN 978-3-642-35288-1 [contents] - [i1]Grégoire Montavon, Klaus-Robert Müller:
Learning Feature Hierarchies with Centered Deep Boltzmann Machines. CoRR abs/1203.3783 (2012) - 2011
- [j1]Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller:
Kernel Analysis of Deep Networks. J. Mach. Learn. Res. 12: 2563-2581 (2011) - 2010
- [c1]Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller:
Layer-wise analysis of deep networks with Gaussian kernels. NIPS 2010: 1678-1686
Coauthor Index
Sebastian Lapuschkin
aka: Sebastian Bach
aka: Sebastian Bach
[j20] [j19] [j17] [d2] [d1] [i41] [i40] [i39] [i38] [j16] [c15] [c13] [i36] [i34] [j15] [j14] [j13] [j12] [c12] [c11] [i33] [i32] [i31] [j11] [j10] [i30] [i28] [j8] [c10] [i27] [i26] [i25] [i24] [i22] [j7] [p8] [p7] [p5] [e2] [i21] [i20] [i19] [j6] [i18] [i17] [i16] [i15] [j5] [j4] [c9] [i14] [i13] [i12] [i11] [j3] [c8] [c7] [c6] [c5] [c4] [i10] [i9] [i8] [i7] [i6] [i5] [i4] [i3] [i2] [j2] [c3] [c2] [p4] [p3] [p2] [p1] [e1] [i1] [j1] [c1]
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