Syntactic vs Semantic Linear Abstraction and Refinement of
Neural Networks

D 2023

Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks

CHAU, Calvin, Jan KŘETÍNSKÝ and Stefanie MOHR

Basic information

Original name

Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks

Authors

CHAU, Calvin, Jan KŘETÍNSKÝ (203 Czech Republic, belonging to the institution) and Stefanie MOHR

Edition

Singapore, Automated Technology for Verification and Analysis. ATVA 2023, p. 401-421, 21 pp. 2023

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/23:00133938

Organization unit

Faculty of Informatics

ISBN

978-3-031-45328-1

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-45329-8_19

Keywords in English

Abstraction; Machine learning; Neural network

Abstract

V originále

Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections between neurons) or semantically (on the activation values of neurons for various inputs). Unfortunately, the previous approaches only achieve moderate reductions, when implemented at all. In this work, we provide a more flexible framework, where a neuron can be replaced with a linear combination of other neurons, improving the reduction. We apply this approach both on syntactic and semantic abstractions, and implement and evaluate them experimentally. Further, we introduce a refinement method for our abstractions, allowing for finding a better balance between reduction and precision.

Citovat

CHAU, Calvin, Jan KŘETÍNSKÝ and Stefanie MOHR. Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks. Online. In Automated Technology for Verification and Analysis. ATVA 2023. Singapore: Springer, 2023, p. 401-421. ISBN 978-3-031-45328-1. Available from: https://dx.doi.org/10.1007/978-3-031-45329-8_19.

@inproceedings{2392233,
   author = {Chau, Calvin and Křetínský, Jan and Mohr, Stefanie},
   address = {Singapore},
   booktitle = {Automated Technology for Verification and Analysis. ATVA 2023},
   doi = {http://dx.doi.org/10.1007/978-3-031-45329-8_19},
   keywords = {Abstraction; Machine learning; Neural network},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Singapore},
   isbn = {978-3-031-45328-1},
   pages = {401-421},
   publisher = {Springer},
   title = {Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks},
   year = {2023}
}

TY  - JOUR
ID  - 2392233
AU  - Chau, Calvin - Křetínský, Jan - Mohr, Stefanie
PY  - 2023
TI  - Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks
PB  - Springer
CY  - Singapore
SN  - 9783031453281
KW  - Abstraction
KW  - Machine learning
KW  - Neural network
N2  - Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections between neurons) or semantically (on the activation values of neurons for various inputs). Unfortunately, the previous approaches only achieve moderate reductions, when implemented at all. In this work, we provide a more flexible framework, where a neuron can be replaced with a linear combination of other neurons, improving the reduction. We apply this approach both on syntactic and semantic abstractions, and implement and evaluate them experimentally. Further, we introduce a refinement method for our abstractions, allowing for finding a better balance between reduction and precision.
ER  -

CHAU, Calvin, Jan KŘETÍNSKÝ and Stefanie MOHR. Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks. Online. In \textit{Automated Technology for Verification and Analysis. ATVA 2023}. Singapore: Springer, 2023, p.~401-421. ISBN~978-3-031-45328-1. Available from: https://dx.doi.org/10.1007/978-3-031-45329-8\_{}19.

Detailed Information on Publication Record