Decomposing convolutional neural networks into reusable and replaceable modules-Reference-Cited by-同舟云学术

Decomposing convolutional neural networks into reusable and replaceable modules

Published:2022-05-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 44th International Conference on Software Engineering
language:
Short-container-title:

Author:

Pan Rangeet¹,Rajan Hridesh¹

Affiliation:

1. Iowa State University

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3510003.3510051

Reference42 articles.

1. Pierre America . 1990 . Designing an object-oriented programming language with behavioural subtyping . In Workshop/School/Symposium of the REX Project (Research and Education in Concurrent Systems). Springer, 60--90 . Pierre America. 1990. Designing an object-oriented programming language with behavioural subtyping. In Workshop/School/Symposium of the REX Project (Research and Education in Concurrent Systems). Springer, 60--90.

2. Neural Module Networks

3. Report on the algorithmic language ALGOL 60

4. John W Backus Robert J Beeber Sheldon Best Richard Goldberg Lois M Haibt Harlan L Herrick Robert A Nelson David Sayre Peter B Sheridan H Stern etal 1957. The FORTRAN automatic coding system. In Papers presented at the February 26--28 1957 western joint computer conference: Techniques for reliability. 188--198. John W Backus Robert J Beeber Sheldon Best Richard Goldberg Lois M Haibt Harlan L Herrick Robert A Nelson David Sayre Peter B Sheridan H Stern et al. 1957. The FORTRAN automatic coding system. In Papers presented at the February 26--28 1957 western joint computer conference: Techniques for reliability. 188--198.

5. Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness

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