Explaining and Interpreting LSTMs

Leila Arras, José Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus Robert Müller, Sepp Hochreiter, Wojciech Samek

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

While neural networks have acted as a strong unifying force in the design of modern AI systems, the neural network architectures themselves remain highly heterogeneous due to the variety of tasks to be solved. In this chapter, we explore how to adapt the Layer-wise Relevance Propagation (LRP) technique used for explaining the predictions of feed-forward networks to the LSTM architecture used for sequential data modeling and forecasting. The special accumulators and gated interactions present in the LSTM require both a new propagation scheme and an extension of the underlying theoretical framework to deliver faithful explanations.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages211-238
Number of pages28
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11700 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Explainable artificial intelligence
  • Interpretability
  • LSTM
  • Model transparency
  • Recurrent neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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