Interpretability of Machine Learning Models and Representations: an Introduction

Adrien Bibal; Benoît Frénay

Interpretability of Machine Learning Models and Representations: an Introduction

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Résumé

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.

langue originale	Anglais
titre	24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Sous-titre	ESANN 2016 : Bruges, Belgium, April 27-28-29, 2016 : proceedings
rédacteurs en chef	Michel Verleysen
Lieu de publication	Bruges
Editeur	CIACO
Pages	77-82
ISBN (Electronique)	978-287587027-8
ISBN (imprimé)	978-2-87587-026-1
Etat de la publication	Publié - 2016
Evénement	24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgique Durée: 27 avr. 2016 → 29 mai 2016 Numéro de conférence: 24

Une conférence

Une conférence	24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Titre abrégé	ESANN 2016
Pays/Territoire	Belgique
La ville	Bruges
période	27/04/16 → 29/05/16

Accès au document

2016-Bibal-Interpretability of Machine Learning Models - an IntroductionAccepted author manuscript, 221 KB

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Bibal, A. (Auteur), FRENAY, B. (Promoteur), VANHOOF, W. (Président), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Jury) & Galarraga, L. (Jury), 16 nov. 2020
Student thesis: Doc types › Docteur en Sciences

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Contient cette citation

@inproceedings{612762a86d814268bf8c59d5acc5e1a2,

title = "Interpretability of Machine Learning Models and Representations: an Introduction",

abstract = "Interpretability is often a major concern in machine learning. Althoughmany authors agree with this statement, interpretability is often tackledwith 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 modelsand representations, as well as heuristic-based and user-based approaches.",

author = "Adrien Bibal and Beno{\^i}t Fr{\'e}nay",

year = "2016",

language = "English",

isbn = "978-2-87587-026-1",

pages = "77--82",

editor = "Michel Verleysen",

booktitle = "24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

publisher = "CIACO ",

note = "24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 ; Conference date: 27-04-2016 Through 29-05-2016",

}

Bibal, A & Frénay, B 2016, Interpretability of Machine Learning Models and Representations: an Introduction. Dans M Verleysen (Ed.), 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2016 : Bruges, Belgium, April 27-28-29, 2016 : proceedings. CIACO , Bruges, p. 77-82, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgique, 27/04/16.

Interpretability of Machine Learning Models and Representations: an Introduction. / Bibal, Adrien ; Frénay, Benoît.
24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN 2016 : Bruges, Belgium, April 27-28-29, 2016 : proceedings. Ed. / Michel Verleysen. Bruges: CIACO , 2016. p. 77-82.

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

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T1 - Interpretability of Machine Learning Models and Representations

T2 - 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

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AU - Frénay, Benoît

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PY - 2016

Y1 - 2016

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AB - Interpretability is often a major concern in machine learning. Althoughmany authors agree with this statement, interpretability is often tackledwith 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 modelsand representations, as well as heuristic-based and user-based approaches.

M3 - Conference contribution

SN - 978-2-87587-026-1

SP - 77

EP - 82

BT - 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

A2 - Verleysen, Michel

PB - CIACO

CY - Bruges

Y2 - 27 April 2016 through 29 May 2016

ER -

Interpretability of Machine Learning Models and Representations: an Introduction

Résumé

Une conférence

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Empreinte digitale

Thèses de l'étudiant

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction

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