Apr 25, 2019 · In this paper, we analyze one of the means to increase the performances of ML algorithms which is exploiting data locality.
Machine learning (ML) is probably the first and foremost used technique to deal with the size and complexity of the new generation of data.
Specifically, our approach exploits computational redundancy for the design of recovery instead of using resource redundancy. The presented fault-tolerant task ...
This paper analyzes one of the means to increase the performances of ML algorithms which is exploiting data locality, and identifies some of the ...
Reviewing data access patterns and computational redundancy for machine learning algorithms. dc.contributor.author, Chakroun, Imen. dc.contributor.author ...
Reviewing Data Access Patterns and Computational ... - dblp
dblp.uni-trier.de › corr › abs-1904-11203
Bibliographic details on Reviewing Data Access Patterns and Computational Redundancy for Machine Learning Algorithms.
REVIEWING DATA ACCESS PATTERNS AND COMPUTATIONAL REDUNDANCY FOR MACHINE LEARNING ALGORITHMS Imen Chakroun, Tom Vander Aa and Tom Ashby. cover, AN ETL PATTERN ...
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Altering the access patterns to increase locality can dramatically increase performance of a given algorithm. BIG-bench Machine Learning.
Nov 10, 2023 · In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties.
Altering the access patterns to increase locality can dramatically increase performance of a given algorithm. BIG-bench Machine Learning.