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We present a thorough theoretical analysis of this framework in which we characterize the statistical errors introduced by the "divide" step and control their ...
Abstract. If learning methods are to scale to the massive sizes of modern data sets, it is essential for the field of machine learning to embrace parallel ...
Jul 5, 2011 · We present a thorough theoretical analysis of this framework in which we characterize the statistical errors introduced by the "divide" step and ...
A scalable divide-and-conquer framework for noisy matrix factorization is introduced in which the statistical errors introduced by the "divide" step are ...
Aug 13, 2016 · We present a thorough theoretical analysis of this framework in which we characterize the statistical errors introduced by the "divide" step and ...
Jan 1, 2015 · Inspired by the recent development of matrix factorization methods with rich theory but poor computational complexity and by the relative ease ...
Using DSGD++, we can factor a matrix with 10B entries on 16 compute nodes in around 40 minutes. Keywords-parallel and distributed matrix factorization;.
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Jun 2, 2022 · Bibliographic details on Distributed matrix completion and robust factorization.
Oct 13, 2022 · In this paper, based on the factorization framework, we propose a novel robust matrix completion scheme via using the truncated-quadratic loss ...
Missing: Distributed | Show results with:Distributed
PDF | We discuss parallel and distributed algorithms for large-scale matrix completion on problems with millions of rows, millions of columns, and.