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The Cartesian product is a technique used in a wide range of algorithms. Performing a Cartesian product on a distributed dataset poses a variety of challenges. The process requires enormous amounts of data to be shuffled between nodes, and the computation effectively squares the size of the input data. As a result, for large scale analyses, this sort of pairwise operation is likely to be the bottleneck...
Although deep learning has achieved outstanding performances on several difficult machine learning applications, there are multiple issues that make its application on new problems difficult: speed of training, local minima, and manual selection of hyper-parameters. To overcome these problems, this paper proposes a new evolutionary method, EvoAE, to train auto encoders for deep learning networks....
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