Interpretability of Machine Learning Models and Representations: an Introduction

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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
Subtitle of host publicationESANN 2016 : Bruges, Belgium, April 27-28-29, 2016 : proceedings
EditorsMichel Verleysen
Place of PublicationBruges
PublisherCIACO
Pages77-82
ISBN (Electronic)978-287587027-8
ISBN (Print)978-2-87587-026-1
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

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