Interpretable On-the-Fly Repair of Deep Neural Classifiers-Reference-Cited by-同舟云学术

Interpretable On-the-Fly Repair of Deep Neural Classifiers

Published:2023-12-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 1st International Workshop on Dependability and Trustworthiness of Safety-Critical Systems with Machine Learned Components
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Author:

Mohasel Arjomandi Hossein¹,Jabbarvand Reyhaneh²

Affiliation:

1. University of Illinois at Urbana-Champaign, Urbana, USA

2. University of Illinois at Urbana-Champaign, Chicago, USA

Publisher

ACM

Reference14 articles.

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2. Uncertainty-aware Prediction Validator in Deep Learning Models for Cyber-physical System Data

3. Jakob Gawlikowski , Cedrique Rovile Njieutcheu Tassi , Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al. 2023 . A survey of uncertainty in deep neural networks. Artificial Intelligence Review ( 2023 ), 1-77. Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, et al. 2023. A survey of uncertainty in deep neural networks. Artificial Intelligence Review ( 2023 ), 1-77.

4. Pang Wei Koh and Percy Liang . 2017 . Understanding black-box predictions via influence functions . In International conference on machine learning. PMLR. Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In International conference on machine learning. PMLR.