2.8k citations · 9.6k across the 55 of their papers we have counts for
5 papers · 1 filter
"What is Relevant in a Text Document?": An Interpretable Machine Learning Approach
Leila Arras, Franziska Horn, Grégoire Montavon +2
Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatic…
Interpreting the Predictions of Complex ML Models by Layer-wise Relevance Propagation
Wojciech Samek, Grégoire Montavon, Alexander Binder +2
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…
Identifying individual facial expressions by deconstructing a neural network
Farhad Arbabzadah, Grégoire Montavon, Klaus-Robert Müller +1
This paper focuses on the problem of explaining predictions of psychological attributes such as attractiveness, happiness, confidence and intelligence from face photographs using d…
Explaining Predictions of Non-Linear Classifiers in NLP
Leila Arras, Franziska Horn, Grégoire Montavon +2
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we…
Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers
Alexander Binder, Grégoire Montavon, Sebastian Bach +2
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 fo…