Interpretability of Machine Learning Models and Representations: an Introduction

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

636 Downloads (Pure)

Abstract

Interpretability is often a major concern in machine learning. Although
many authors agree with this statement, interpretability is often tackled
with intuitive arguments, distinct (yet related) terms and heuristic quan-
tifications. This short survey aims to clarify the concepts related to interpretability and emphasises the distinction between interpreting models
and representations, as well as heuristic-based and user-based approaches.
Original languageEnglish
Title of host publication24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Place of PublicationBruges
Pages77-82
Publication statusPublished - 2016
Event24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 27 Apr 201629 May 2016
Conference number: 24

Conference

Conference24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN 2016
CountryBelgium
CityBruges
Period27/04/1629/05/16

Fingerprint Dive into the research topics of 'Interpretability of Machine Learning Models and Representations: an Introduction'. Together they form a unique fingerprint.

  • Cite this

    Bibal, A., & Frenay, B. (2016). Interpretability of Machine Learning Models and Representations: an Introduction. In 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 77-82).