2.8k citations · 9.6k across the 55 of their papers we have counts for
4 papers · 1 filter
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. The…
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 Patch-Based Learning by Explaining Predictions
Christopher Anders, Grégoire Montavon, Wojciech Samek +1
Deep networks are able to learn highly predictive models of video data. Due to video length, a common strategy is to train them on small video snippets. We apply the deep Taylor /…
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
Jacob Kauffmann, Klaus-Robert Müller, Grégoire Montavon
A common machine learning task is to discriminate between normal and anomalous data points. In practice, it is not always sufficient to reach high accuracy at this task, one also w…