2.8k citations · 13.3k across the 90 of their papers we have counts for
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"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…
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller +2
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers…
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…
Sharing Hash Codes for Multiple Purposes
Wikor Pronobis, Danny Panknin, Johannes Kirschnick +6
Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissim…
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…