Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications.
Building on this we show how explanation methods can be used in applications to understand predictions for miss-classified samples, to compare algorithms or networks, and to examine the focus of networks.
1 code implementation • 13 Aug 2018 • Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.
Saliency methods aim to explain the predictions of deep neural networks.
We show that these methods do not produce the theoretically correct explanation for a linear model.
Training of one-vs.-rest SVMs can be parallelized over the number of classes in a straight forward way.