Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI-Reference-Cited by-同舟云学术

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Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI

Published:2021-12 Issue: Volume:233 Page:107530
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Kenny Eoin M.^ORCID,Keane Mark T.^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference58 articles.

1. Ai at google: our principles;Pichai,2018

2. Explanation in artificial intelligence: Insights from the social sciences;Miller;Artif. Intell.,2019

3. R.R. Hoffman, S.T. Mueller, G. Klein, J. Litman, Metrics for explainable ai: Challenges and prospects, arXiv preprint arXiv:1812.04608.

4. The mythos of model interpretability;Lipton;Queue,2018

5. Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda;Abdul,2018

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4. A Survey of Text Classification With Transformers: How Wide? How Large? How Long? How Accurate? How Expensive? How Safe?;IEEE Access;2024

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