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Communications of the ACM

Research Archive


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The Research archive provides access to all Research articles published in past issues of Communications of the ACM.

May 2019


From Communications of the ACM

Technical Perspective: Compressing Matrices for Large-Scale Machine Learning

Demand for more powerful big data analytics solutions has spurred the development of novel programming models, abstractions, and platforms. "Scaling Machine Learning via Compressed Linear Algebra" seeks to address many of these…


From Communications of the ACM

Compressed Linear Algebra for Declarative Large-Scale Machine Learning

Compressed Linear Algebra for Declarative Large-Scale Machine Learning

General-purpose compression struggles to achieve both good compression ratios and fast decompression for blockwise uncompressed operations. Therefore, we introduce Compressed Linear Algebra for lossless matrix compression.