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In recent works, authors discussed upper bound for unequal capacity network called zig-zag network. It shows that there are several variants of upper bounds starting from cut set bound according to topology of the zig-zag network. We propose an universal coding structure, on which the same detection and decoding strategy can be applied to achieve any particular variants of cut set bound.
Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of predictive (diagnostic) models using patients' data from clinical studies requires effective integration of genetic, clinical and demographic data. Typically all heterogeneous inputs are properly encoded and mapped onto a single feature vector, used for estimating (training) a predictive model....
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