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We focus on sorting, which is the building block of many machine learning algorithms, and propose a novel distributed sorting algorithm, named CodedTeraSort, which substantially improves the execution time of the TeraSort benchmark in Hadoop MapReduce. The key idea of CodedTeraSort is to impose structured redundancy in data, in order to enable in-network coding opportunities that overcome the data...
Microstructural information plays a key role in governing the dominant physics for various applications involving fracture networks. Resolving the interactions of thousands of interconnected sub-micron scale fractures is computationally intensive, and is intractable with current technologies. Coarsening of the domain and simplification of the physics are two commonly used workarounds, but these methods...
A general analytical framework is described for melding graph-theoretical algorithms and machine learning technologies. A main goal is to extract latent relationships and other forms of knowledge from immense, noisy, and often incomplete data. Several exemplars illustrate the overall process, and highlight critical methodological decision points encountered across a variety of application domains.
In this work, a framework based on maximum likelihood estimation and mutual information is proposed to design a metaheuristic. A multilevel decomposition of metaheuristics is proposed that allow to have a unified vision on this optimization approach. Then, a new layer based on machine learning is added to take profit from the evolution of the algorithm to adapt it to the considered problem to alleviate...
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