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Safety and Trust in Artificial Intelligence with Abstract Interpretation
- Default Title
Description
As a result, researchers have spent considerable time developing automated methods for building safe and trustworthy DNNs. Abstract interpretation has emerged as the most popular framework for efficiently analyzing realistic DNNs among the various approaches. However, due to fundamental differences in the computational structure of DNNs compared to traditional programs, developing efficient DNN analyzers has required tackling significantly different research challenges than those encountered for programs.
In this monograph, state-of-the-art approaches based on abstract interpretation for analyzing DNNs are described. These approaches include the design of new abstract domains, synthesis of novel abstract transformers, abstraction refinement, and incremental analysis. Discussed is how the analysis results can be used to: (i) formally check whether a trained DNN satisfies desired output and gradient-based safety properties, (ii) guide the model updates during training towards satisfying safety properties, and (iii) reliably explain and interpret the black-box workings of DNNs.
Product details
Binding:
Paperback
Number of Pages:
174
Release Date:
2025-06-26
Publication Date:
2025-06-26
Publisher:
Now Publishers Inc
Languages:
Original:
English
ISBN10:
1638285861
ISBN13:
9781638285861
Weight:
275 g
Height:
156 cm
Width:
234 cm
Thickness:
10 cm
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