Interpretability of Machine Learning Models and Representations: an Introduction

Adrien Bibal; Benoît Frénay

Interpretability of Machine Learning Models and Representations: an Introduction

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

972 Téléchargements (Pure)

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
Auteur: Bibal, A., 16 nov. 2020
Superviseur: FRENAY, B. (Promoteur), VANHOOF, W. (Président), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Personne externe) (Jury) & Galarraga, L. A. (Personne externe) (Jury)
Student thesis: Doc types › Docteur en Sciences

Fichier

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

TY - GEN

T1 - Interpretability of Machine Learning Models and Representations

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

AU - Bibal, Adrien

AU - Frénay, Benoît

N1 - Conference code: 24

PY - 2016

Y1 - 2016

N2 - 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.

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

Accès au document

Empreinte digitale

Thèses de l'étudiant

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

Contient cette citation