TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Acceleration Approaches for Big Data Analysis

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 25th IEEE International Conference on Image Processing (ICIP)
KustantajaIEEE
ISBN (elektroninen)978-1-4799-7061-2
DOI - pysyväislinkit
TilaJulkaistu - 6 syyskuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Image Processing -
Kesto: 7 lokakuuta 201810 lokakuuta 2018

Julkaisusarja

Nimi
ISSN (elektroninen)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Ajanjakso7/10/1810/10/18

Tiivistelmä

The massive size of data that needs to be processed by Machine Learning models nowadays sets new challenges related to their computational complexity and memory footprint. These challenges span all processing steps involved in the application of the related models, i.e., from the fundamental processing steps needed to evaluate distances of vectors, to the optimization of large-scale systems, e.g. for non-linear regression using kernels, or the speed up of deep learning models formed by billions of parameters. In order to address these challenges, new approximate solutions have been recently proposed based on matrix/tensor decompositions, randomization and quantization strategies. This paper provides a comprehensive review of the related methodologies and discusses their connections.

Julkaisufoorumi-taso