Search Results for author: Grégoire Montavon

Found 32 papers, 13 papers with code

XAI for Transformers: Better Explanations through Conservative Propagation

1 code implementation15 Feb 2022 Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf

Transformers have become an important workhorse of machine learning, with numerous applications.

Deep learning for surrogate modelling of 2D mantle convection

no code implementations23 Aug 2021 Siddhant Agarwal, Nicola Tosi, Pan Kessel, Doris Breuer, Grégoire Montavon

Using a dataset of 10, 525 two-dimensional simulations of the thermal evolution of the mantle of a Mars-like planet, we show that deep learning techniques can produce reliable parameterized surrogates (i. e. surrogates that predict state variables such as temperature based only on parameters) of the underlying partial differential equations.

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

1 code implementation9 Jun 2021 Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon

Domain shifts in the training data are common in practical applications of machine learning, they occur for instance when the data is coming from different sources.

A Unifying Review of Deep and Shallow Anomaly Detection

no code implementations24 Sep 2020 Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller

Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.

Anomaly Detection

GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis

1 code implementation12 Aug 2020 Kateryna Melnyk, Stefan Klus, Grégoire Montavon, Tim Conrad

We demonstrate that our method can capture temporary changes in the time-evolving graph on both created synthetic data and real-world data.

The Clever Hans Effect in Anomaly Detection

no code implementations18 Jun 2020 Jacob Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller

The 'Clever Hans' effect occurs when the learned model produces correct predictions based on the 'wrong' features.

Anomaly Detection Outlier Detection

Higher-Order Explanations of Graph Neural Networks via Relevant Walks

no code implementations5 Jun 2020 Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon

In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i. e. by identifying groups of edges that jointly contribute to the prediction.

Image Classification Sentiment Analysis

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

no code implementations17 Mar 2020 Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders, Klaus-Robert Müller

With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI.

Interpretable Machine Learning

Building and Interpreting Deep Similarity Models

1 code implementation11 Mar 2020 Oliver Eberle, Jochen Büttner, Florian Kräutli, Klaus-Robert Müller, Matteo Valleriani, Grégoire Montavon

Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'.

Anomaly Detection

Explaining and Interpreting LSTMs

no code implementations25 Sep 2019 Leila Arras, Jose A. Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, Wojciech Samek

While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved.

From Clustering to Cluster Explanations via Neural Networks

no code implementations18 Jun 2019 Jacob Kauffmann, Malte Esders, Lukas Ruff, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

Cluster predictions of the obtained networks can then be quickly and accurately attributed to the input features.

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

1 code implementation26 Feb 2019 Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior.

iNNvestigate neural networks!

1 code implementation13 Aug 2018 Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans

The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.

Interpretable Machine Learning

Understanding Patch-Based Learning by Explaining Predictions

no code implementations11 Jun 2018 Christopher Anders, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

We apply the deep Taylor / LRP technique to understand the deep network's classification decisions, and identify a "border effect": a tendency of the classifier to look mainly at the bordering frames of the input.

General Classification

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

no code implementations16 May 2018 Jacob Kauffmann, Klaus-Robert Müller, Grégoire Montavon

The proposed One-Class DTD is applicable to a number of common distance-based SVM kernels and is able to reliably explain a wide set of data anomalies.

Edge Detection

Discovering topics in text datasets by visualizing relevant words

1 code implementation18 Jul 2017 Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents.

Exploring text datasets by visualizing relevant words

2 code implementations17 Jul 2017 Franziska Horn, Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

When working with a new dataset, it is important to first explore and familiarize oneself with it, before applying any advanced machine learning algorithms.

Methods for Interpreting and Understanding Deep Neural Networks

no code implementations24 Jun 2017 Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions.

Explaining Recurrent Neural Network Predictions in Sentiment Analysis

1 code implementation WS 2017 Leila Arras, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions.

General Classification Interpretable Machine Learning +1

Wasserstein Training of Restricted Boltzmann Machines

no code implementations NeurIPS 2016 Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi

This metric between observations can then be used to define the Wasserstein distance between the distribution induced by the Boltzmann machine on the one hand, and that given by the training sample on the other hand.


Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation

no code implementations24 Nov 2016 Wojciech Samek, Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Klaus-Robert Müller

Complex nonlinear models such as deep neural network (DNNs) have become an important tool for image classification, speech recognition, natural language processing, and many other fields of application.

General Classification Image Classification +2

Identifying individual facial expressions by deconstructing a neural network

no code implementations23 Jun 2016 Farhad Arbabzadah, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

We further observe that the explanation method provides important insights into the nature of features of the base model, which allow one to assess the aptitude of the base model for a given transfer learning task.

Gender Prediction Transfer Learning

Explaining Predictions of Non-Linear Classifiers in NLP

1 code implementation WS 2016 Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek

Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables.

General Classification Image Classification +1

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

no code implementations4 Apr 2016 Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller, Wojciech Samek

Layer-wise relevance propagation is a framework which allows to decompose the prediction of a deep neural network computed over a sample, e. g. an image, down to relevance scores for the single input dimensions of the sample such as subpixels of an image.

Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

4 code implementations8 Dec 2015 Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, Klaus-Robert Müller

Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures.

Action Recognition Classification +3

Evaluating the visualization of what a Deep Neural Network has learned

1 code implementation21 Sep 2015 Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller

Our main result is that the recently proposed Layer-wise Relevance Propagation (LRP) algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method.

Classification General Classification +2

Wasserstein Training of Boltzmann Machines

no code implementations7 Jul 2015 Grégoire Montavon, Klaus-Robert Müller, Marco Cuturi

The Boltzmann machine provides a useful framework to learn highly complex, multimodal and multiscale data distributions that occur in the real world.


Layer-wise analysis of deep networks with Gaussian kernels

no code implementations NeurIPS 2010 Grégoire Montavon, Klaus-Robert Müller, Mikio L. Braun

Deep networks can potentially express a learning problem more efficiently than local learning machines.

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