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A compositional approach for real-time machine learning:
, In:
Proceedings of the 17th ACM-IEEE International Conference on Formal Methods and Models for System Design
,
Allen, Nathan
;
Raje, Yash
;
Ro, Jin Woo
. - p. 1-5 , 2019
Link:
https://dl.acm.org/doi/10.1145/3359986.3361204
RT T1
Proceedings of the 17th ACM-IEEE International Conference on Formal Methods and Models for System Design
: T1
A compositional approach for real-time machine learning
UL https://suche.suub.uni-bremen.de/peid=acm-3361204&Exemplar=1&LAN=DE A1 Allen, Nathan A1 Raje, Yash A1 Ro, Jin Woo A1 Roop, Partha PB ACM YR 2019 K1 hardware K1 machine learning K1 neural networks K1 semantics K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Computer systems organization K1 Real-time systems K1 Real-time system architecture K1 Software and its engineering K1 Software notations and tools K1 Compilers K1 Source code generation K1 Formal language definitions K1 Semantics SP 1 OP 5 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3359986.3361204 DO https://dl.acm.org/doi/10.1145/3359986.3361204 SF ELIB - SuUB Bremen
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