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compressed linear algebra for large-scale machine learning compressed linear algebra for large-scale machine learning ahmed elgohary ∗, matthias boehm , peter j. haas , frederick r. reiss , berthold reinwald ibm research – almaden; san jose, ca, usa university of maryland; college park, md, usa abstract
large-scale machine learning (ml) algorithms are often iterative, using repeated read-only data access and i/o- bound matrix-vector multiplications to converge to an opti- mal model. it is crucial for performance to fit the data into single-node or distributed main memory. general-purpose, heavy- and...
http://www.vldb.org/pvldb/vol9/p960-elgohary.pdf
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