ML interpretability: Simple isn't easy-Reference-Cited by-同舟云学术

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ML interpretability: Simple isn't easy

Published:2024-02 Issue: Volume:103 Page:159-167
ISSN:0039-3681
Container-title:Studies in History and Philosophy of Science
language:en
Short-container-title:Studies in History and Philosophy of Science

Author:

Räz Tim

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Elsevier BV

Reference36 articles.

1. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI);Adadi;IEEE Access,2018

2. Statistical mechanics of deep learning;Bahri;Annual Review of Condensed Matter Physics,2020

3. Explicating objectual understanding: Taking degrees seriously;Baumberger;Journal for General Philosophy of Science,2019

4. What is understanding? An overview of recent debates in epistemology and philosophy of science;Baumberger,2017

5. Philosophy of science at sea: Clarifying the interpretability of machine learning;Beisbart;Philosophy Compass,2022

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions;Frontiers in Microbiology;2024-02-13

2. Pixel-Grounded Prototypical Part Networks;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

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