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Suitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments

contributor authorGui, Guan
contributor authorLi Xu
contributor authorAdachi, Fumiyuki
date accessioned2020-03-12T22:16:07Z
date available2020-03-12T22:16:07Z
date issued2014
identifier other7000208.pdf
identifier urihttp://libsearch.um.ac.ir:80/fum/handle/fum/1077078?show=full
formatgeneral
languageEnglish
publisherIEEE
titleSuitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments
typeConference Paper
contenttypeMetadata Only
identifier padid8213316
subject keywordslearning (artificial intelligence)
subject keywordsn program compilers
subject keywordsn software libraries
subject keywordsn sorting
subject keywordsn Nitro
subject keywordsn adaptive code variant tuning framework
subject keywordsn autotuning systems
subject keywordsn graph computations
subject keywordsn irregular GPU benchmarks
subject keywordsn library interface
subject keywordsn machine learning
subject keywordsn meta-information
subject keywordsn optimization criteria
subject keywordsn overhead reduction
subject keywordsn programmer-directed autotuning framework
subject keywordsn search space navigation
subject keywordsn sorting
subject keywordsn sparse numerical methods
subject keywordsn training time reduction
subject keywordsn Li
identifier doi10.1109/IPDPS.2014.59
journal titleigh Mobility Wireless Communications (HMWC), 2014 International Workshop on
filesize916454
citations0


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