Work

Interpretability of machine learning models and representations: an introduction.

Year: 2016

Type: conference-paper

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.

Source: Repository of the University of Namur

Authors: Adrien Bibal, Benoît Frénay‬

Institutions: UCLouvain, University of Namur

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2902011388

FWCI: 4.635

Cites:

Cited by: 77

Related to: 10