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机器学习模型可解释性方法,应用与安全研究综述
Translated title of the contribution
:
Survey on Techniques, Applications and Security of Machine Learning Interpretability
Shouling Ji
, Jinfeng Li
, Tianyu Du
,
Bo Li
Siebel School of Computing and Data Science
Research output
:
Contribution to journal
›
Review article
›
peer-review
Overview
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Dive into the research topics of 'Survey on Techniques, Applications and Security of Machine Learning Interpretability'. Together they form a unique fingerprint.
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Keyphrases
Technique Application
100%
Interpretable Machine Learning
100%
Machine Learning
66%
Interpretation Method
66%
Model Interpretation
66%
Research on Models
66%
Real-time Task
33%
Plethora
33%
Related Technology
33%
Interpretation Problem
33%
Critical Security
33%
Machine-made
33%
Machine Learning Models
33%
Scientific Classification
33%
Technology Analysis
33%
Model Interpretability
33%
Working Mechanism
33%
Computer Science
Interpretability
100%
Machine Learning
100%
Learning System
100%
Research Community
14%
Potential Application
14%
Research Direction
14%
Related Technology
14%
Widespread Application
14%
Interpretable Machine Learning
14%
Psychology
Learning Model
100%