2.8k citations · 19.2k across the 71 of their papers we have counts for
15 papers · 1 filter
Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders +3
Explanation methods aim to make neural networks more trustworthy and interpretable. In this paper, we demonstrate a property of explanation methods which is disconcerting for both…
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Jacob Kauffmann, Grégoire Montavon, Luiz Alberto Lima +3
Detecting and explaining anomalies is a challenging effort. This holds especially true when data exhibits strong dependencies and single measurements need to be assessed and analyz…
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
Sebastian Lapuschkin, Alexander Binder, Klaus-Robert Müller +1
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box ma…
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda +3
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical spa…
Learning how to explain neural networks: PatternNet and PatternAttribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber +4
DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a line…
Predicting Pairwise Relations with Neural Similarity Encoders
Franziska Horn, Klaus-Robert Müller
Matrix factorization is at the heart of many machine learning algorithms, for example, dimensionality reduction (e.g. kernel PCA) or recommender systems relying on collaborative fi…