Journal ArticleDOI
Visual Interpretation of Kernel-Based Prediction Models.
Katja Hansen,David Baehrens,Timon Schroeter,Matthias Rupp,Matthias Rupp,Klaus-Robert Müller,Klaus-Robert Müller +6 more
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TLDR
A method for the interpretation of kernel‐based prediction models that helps to assess the domain of applicability of a model, to judge the reliability of a prediction, and to determine relevant molecular features is developed and validated.Abstract:
Statistical models are frequently used to estimate molecular properties, e.g., to establish quantitative structure-activity and structure-property relationships. For such models, interpretability, knowledge of the domain of applicability, and an estimate of confidence in the predictions are essential. We develop and validate a method for the interpretation of kernel-based prediction models. As a consequence of interpretability, the method helps to assess the domain of applicability of a model, to judge the reliability of a prediction, and to determine relevant molecular features. Increased interpretability also facilitates the acceptance of such models. Our method is based on visualization: For each prediction, the most contributing training samples are computed and visualized. We quantitatively show the effectiveness of our approach by conducting a questionnaire study with 71 participants, resulting in significant improvements of the participants' ability to distinguish between correct and incorrect predictions of a Gaussian process model for Ames mutagenicity.read more
Citations
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Journal ArticleDOI
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.
Sebastian Bach,Alexander Binder,Grégoire Montavon,Frederick Klauschen,Klaus-Robert Müller,Wojciech Samek +5 more
TL;DR: This work proposes a general solution to the problem of understanding classification decisions by pixel-wise decomposition of nonlinear classifiers by introducing a methodology that allows to visualize the contributions of single pixels to predictions for kernel-based classifiers over Bag of Words features and for multilayered neural networks.
Journal ArticleDOI
Methods for interpreting and understanding deep neural networks
Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +4 more
TL;DR: The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which the author provides theory, recommendations, and tricks, to make most efficient use of it on real data.
Journal ArticleDOI
Explaining nonlinear classification decisions with deep Taylor decomposition
TL;DR: A novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements by backpropagating the explanations from the output to the input layer is introduced.
Journal ArticleDOI
Evaluating the Visualization of What a Deep Neural Network Has Learned
TL;DR: In this article, a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps is presented, and the authors compare heatmaps computed by three different methods on the SUN397, ILSVRC2012, and MIT Places data sets.
Posted Content
Evaluating the visualization of what a Deep Neural Network has learned
TL;DR: A general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps and shows that the recently proposed layer-wise relevance propagation 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.
References
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Journal ArticleDOI
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