Scaling of neural network inferencing by efficient storage and retrieval of outputs

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Scaling of neural network inferencing by efficient storage and retrieval of outputs

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Symposium on Applied Computing archive
Proceedings of the 1997 ACM symposium on Applied computing table of contents

San Jose, California, United States

Pages: 10 - 14

Year of Publication: 1997

ISBN:0-89791-850-9

Authors

Bryn Lewis	La Trobe University, Victoria, Australia
Andrew Stranieri	University of Ballarat, Victoria, Australia
John Zeleznikow	La Trobe University, Victoria, Australia

Sponsors

SIGCUE: ACM Special Interest Group on Computer Uses In Education
SIGADA: ACM Special Interest Group on Ada Programming Language
SIGAPP: ACM Special Interest Group on Applied Computing
SIGBIO: ACM Special Interest Group on Biomedical Computing

Publisher

ACM New York, NY, USA

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	3	t?,~,dc. S. L. and 14ulchings, t3. L. t';PGA-based st<,chaslic neuralmetworks implementation. Pre~c'eedin~,s of Electronic and Computer Engineering, 1994. IEEE Computer Society Press, 1994. p. 189- ! 98.
	2	Bonnisone, P. P. and Chiand, K. H. A Knowledgebased system view of fuzzy logic controllers. Fuzzy sets, neural networks and soft computing. New York : Van Nostrand Reinhold, 1994. p. 296-310.
	4	Toulmin, S. 1958. The Uses of Arguments. Cambridge University Press. Cambridge.
	1	John Zeleznikow , Andrew Stranieri, The split-up system: integrating neural networks and rule-based reasoning in the legal domain, Proceedings of the 5th international conference on Artificial intelligence and law, p.185-194, May 21-24, 1995, College Park, Maryland, United States [doi>10.1145/222092.222235]