Grégoire Montavon

research

∙ 04/12/2023

Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks

Explainable AI has become a popular tool for validating machine learning...

0 Lorenz Linhardt, et al. ∙

research

∙ 03/11/2023

Explainable AI for Time Series via Virtual Inspection Layers

The field of eXplainable Artificial Intelligence (XAI) has greatly advan...

0 Johanna Vielhaben, et al. ∙

research

∙ 12/30/2022

Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces

Explainable AI transforms opaque decision strategies of ML models into e...

0 Pattarawat Chormai, et al. ∙

research

∙ 11/22/2022

Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations

While the evaluation of explanations is an important step towards trustw...

0 Alexander Binder, et al. ∙

research

∙ 02/15/2022

XAI for Transformers: Better Explanations through Conservative Propagation

Transformers have become an important workhorse of machine learning, wit...

59 Ameen Ali, et al. ∙

research

∙ 12/21/2021

Toward Explainable AI for Regression Models

In addition to the impressive predictive power of machine learning (ML) ...

83 Simon Letzgus, et al. ∙

research

∙ 08/23/2021

Deep learning for surrogate modelling of 2D mantle convection

Traditionally, 1D models based on scaling laws have been used to paramet...

3 Siddhant Agarwal, et al. ∙

research

∙ 06/09/2021

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

Domain shifts in the training data are common in practical applications ...

59 Léo Andéol, et al. ∙

research

∙ 09/24/2020

A Unifying Review of Deep and Shallow Anomaly Detection

Deep learning approaches to anomaly detection have recently improved the...

49 Lukas Ruff, et al. ∙

research

∙ 08/12/2020

GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis

More and more diseases have been found to be strongly correlated with di...

0 Kateryna Melnyk, et al. ∙

research

∙ 06/18/2020

The Clever Hans Effect in Anomaly Detection

The 'Clever Hans' effect occurs when the learned model produces correct ...

21 Jacob Kauffmann, et al. ∙

research

∙ 06/05/2020

XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks

Graph Neural Networks (GNNs) are a popular approach for predicting graph...

0 Thomas Schnake, et al. ∙

research

∙ 03/17/2020

Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond

With the broader and highly successful usage of machine learning in indu...

7 Wojciech Samek, et al. ∙

research

∙ 03/11/2020

Building and Interpreting Deep Similarity Models

Many learning algorithms such as kernel machines, nearest neighbors, clu...

0 Oliver Eberle, et al. ∙

research

∙ 09/25/2019

Explaining and Interpreting LSTMs

While neural networks have acted as a strong unifying force in the desig...

17 Leila Arras, et al. ∙

research

∙ 06/18/2019

From Clustering to Cluster Explanations via Neural Networks

A wealth of algorithms have been developed to extract natural cluster st...

6 Jacob Kauffmann, et al. ∙

research

∙ 02/26/2019

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application prob...

0 Sebastian Lapuschkin, et al. ∙

research

∙ 08/13/2018

iNNvestigate neural networks!

In recent years, deep neural networks have revolutionized many applicati...

0 Maximilian Alber, et al. ∙

research

∙ 06/29/2018

Unsupervised Detection and Explanation of Latent-class Contextual Anomalies

Detecting and explaining anomalies is a challenging effort. This holds e...

0 Jacob Kauffmann, et al. ∙

research

∙ 06/11/2018

Understanding Patch-Based Learning by Explaining Predictions

Deep networks are able to learn highly predictive models of video data. ...

0 Christopher Anders, et al. ∙

research

∙ 05/16/2018

Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models

A common machine learning task is to discriminate between normal and ano...

0 Jacob Kauffmann, et al. ∙

research

∙ 07/18/2017

Discovering topics in text datasets by visualizing relevant words

When dealing with large collections of documents, it is imperative to qu...

0 Franziska Horn, et al. ∙

research

∙ 07/17/2017

Exploring text datasets by visualizing relevant words

When working with a new dataset, it is important to first explore and fa...

0 Franziska Horn, et al. ∙

research

∙ 06/24/2017

Methods for Interpreting and Understanding Deep Neural Networks

This paper provides an entry point to the problem of interpreting a deep...

0 Grégoire Montavon, et al. ∙

research

∙ 06/22/2017

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

Recently, a technique called Layer-wise Relevance Propagation (LRP) was ...

0 Leila Arras, et al. ∙

research

∙ 12/23/2016

"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach

Text documents can be described by a number of abstract concepts such as...

0 Leila Arras, et al. ∙

research

∙ 06/23/2016

Explaining Predictions of Non-Linear Classifiers in NLP

Layer-wise relevance propagation (LRP) is a recently proposed technique ...

0 Leila Arras, et al. ∙

research

∙ 06/23/2016

Identifying individual facial expressions by deconstructing a neural network

This paper focuses on the problem of explaining predictions of psycholog...

0 Farhad Arbabzadah, et al. ∙

research

∙ 04/04/2016

Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers

Layer-wise relevance propagation is a framework which allows to decompos...

0 Alexander Binder, et al. ∙

research

∙ 12/08/2015

Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold stand...

0 Grégoire Montavon, et al. ∙

research

∙ 12/01/2015

Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular an...

0 Sebastian Bach, et al. ∙

research

∙ 07/07/2015

Wasserstein Training of Boltzmann Machines

The Boltzmann machine provides a useful framework to learn highly comple...

0 Grégoire Montavon, et al. ∙

research

∙ 03/16/2012

Learning Feature Hierarchies with Centered Deep Boltzmann Machines

Deep Boltzmann machines are in principle powerful models for extracting ...

0 Grégoire Montavon, et al. ∙

Grégoire Montavon

Featured Co-authors