Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Blog Post Jupyter notebooks help data scientists and policy makers analyze real-time COVID-19 data New open source COVID-19 notebooks offer a toolkit that helps developers and data scientists answer important questions about the pandemic. By FREDERICK REISS Published June 25, 2020 For data scientists and policy makers who are analyzing the effects of COVID-19 and trying to come up with actionable plans based on data, the information landscape is overwhelming.
View as: Print Mobile App ACM Digital Library In the Digital Edition Share: Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data into single-node or distributed main memory to enable fast matrix-vector operations. General-purpose compression struggles to achieve both good compression ratios and fast decompression for block-wise uncompressed operations.
Home/Magazine Archive/May 2019 (Vol. 62, No. 5)/Compressed Linear Algebra for Declarative Large-Scale.../Full Text By Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald Communications of the ACM, May 2019, Vol.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
