Grégoire Montavon

Orcid: 0000-0001-7243-6186

Affiliations:
  • TU Berlin, Institute of Software Engineering and Theoretical Computer Science, Germany


According to our database1, Grégoire Montavon authored at least 68 papers between 2010 and 2024.

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Bibliography

2024
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces.
IEEE Trans. Pattern Anal. Mach. Intell., November, 2024

Code and Data for "Historical Insights from Sacrobosco Tables" Project.
Dataset, April, 2024

Code and Data for Historical Insights from Sacrobosco Tables Project.
Dataset, April, 2024

Preemptively pruning Clever-Hans strategies in deep neural networks.
Inf. Fusion, March, 2024

From Clustering to Cluster Explanations via Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., February, 2024

Explainable AI for time series via Virtual Inspection Layers.
Pattern Recognit., 2024

Towards Symbolic XAI - Explanation Through Human Understandable Logical Relationships Between Features.
CoRR, 2024

The Clever Hans Effect in Unsupervised Learning.
CoRR, 2024

MambaLRP: Explaining Selective State Space Sequence Models.
CoRR, 2024

XpertAI: uncovering model strategies for sub-manifolds.
CoRR, 2024

Explaining Predictive Uncertainty by Exposing Second-Order Effects.
CoRR, 2024

2023
Learning domain invariant representations by joint Wasserstein distance minimization.
Neural Networks, October, 2023

Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI.
CoRR, 2023

Relevant Walk Search for Explaining Graph Neural Networks.
Proceedings of the International Conference on Machine Learning, 2023

Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective.
IEEE Signal Process. Mag., 2022

Higher-Order Explanations of Graph Neural Networks via Relevant Walks.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Building and Interpreting Deep Similarity Models.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning.
Datenbank-Spektrum, 2022

Efficient Computation of Higher-Order Subgraph Attribution via Message Passing.
Proceedings of the International Conference on Machine Learning, 2022

XAI for Transformers: Better Explanations through Conservative Propagation.
Proceedings of the International Conference on Machine Learning, 2022

2021
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications.
Proc. IEEE, 2021

A Unifying Review of Deep and Shallow Anomaly Detection.
Proc. IEEE, 2021

Toward Explainable AI for Regression Models.
CoRR, 2021

Deep learning for surrogate modelling of 2D mantle convection.
CoRR, 2021

2020
Towards explaining anomalies: A deep Taylor decomposition of one-class models.
Pattern Recognit., 2020

The Clever Hans Effect in Anomaly Detection.
CoRR, 2020

XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks.
CoRR, 2020

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond.
CoRR, 2020

GraphKKE: graph Kernel Koopman embedding for human microbiome analysis.
Appl. Netw. Sci., 2020

Explaining the Predictions of Unsupervised Learning Models.
Proceedings of the xxAI - Beyond Explainable AI, 2020

2019
Layer-Wise Relevance Propagation: An Overview.
Proceedings of the Explainable AI: Interpreting, 2019

Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison.
Proceedings of the Explainable AI: Interpreting, 2019

Explaining and Interpreting LSTMs.
Proceedings of the Explainable AI: Interpreting, 2019

Understanding Patch-Based Learning of Video Data by Explaining Predictions.
Proceedings of the Explainable AI: Interpreting, 2019

iNNvestigate Neural Networks!
J. Mach. Learn. Res., 2019

Explaining and Interpreting LSTMs.
CoRR, 2019

From Clustering to Cluster Explanations via Neural Networks.
CoRR, 2019

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn.
CoRR, 2019

2018
Methods for interpreting and understanding deep neural networks.
Digit. Signal Process., 2018

Unsupervised Detection and Explanation of Latent-class Contextual Anomalies.
CoRR, 2018

Understanding Patch-Based Learning by Explaining Predictions.
CoRR, 2018

2017
Evaluating the Visualization of What a Deep Neural Network Has Learned.
IEEE Trans. Neural Networks Learn. Syst., 2017

Explaining nonlinear classification decisions with deep Taylor decomposition.
Pattern Recognit., 2017

Discovering topics in text datasets by visualizing relevant words.
CoRR, 2017

Exploring text datasets by visualizing relevant words.
CoRR, 2017

Explaining Recurrent Neural Network Predictions in Sentiment Analysis.
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, 2017

2016
The LRP Toolbox for Artificial Neural Networks.
J. Mach. Learn. Res., 2016

Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation.
CoRR, 2016

"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach.
CoRR, 2016

Explaining Predictions of Non-Linear Classifiers in NLP.
Proceedings of the 1st Workshop on Representation Learning for NLP, 2016

Wasserstein Training of Restricted Boltzmann Machines.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Identifying Individual Facial Expressions by Deconstructing a Neural Network.
Proceedings of the Pattern Recognition - 38th German Conference, 2016

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015
Wasserstein Training of Boltzmann Machines.
CoRR, 2015

2013
On layer-wise representations in deep neural networks.
PhD thesis, 2013

Analyzing Local Structure in Kernel-Based Learning: Explanation, Complexity, and Reliability Assessment.
IEEE Signal Process. Mag., 2013

2012
Identifying Dynamical Systems for Forecasting and Control.
Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Deep Boltzmann Machines and the Centering Trick.
Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Better Representations: Invariant, Disentangled and Reusable.
Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Big Learning and Deep Neural Networks.
Proceedings of the Neural Networks: Tricks of the Trade - Second Edition, 2012

Deep Boltzmann Machines as Feed-Forward Hierarchies.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Learning Feature Hierarchies with Centered Deep Boltzmann Machines
CoRR, 2012

Learning Invariant Representations of Molecules for Atomization Energy Prediction.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Kernel Analysis of Deep Networks.
J. Mach. Learn. Res., 2011

2010
Layer-wise analysis of deep networks with Gaussian kernels.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010


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