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Admixture models are "mixtures of mixtures" that decompose an object into multiple latent components, with the component proportions varying stochastically across objects. Recent work in machine learning has successfully developed admixture models for text, and work in population genetics has developed such models to analyze complex groups of individuals having mixed ancestry. We introduce...
There is much interest into joint runtime and energy optimization of implementations of signal processing algorithms. Applications in domains such as embedded computing, sensor networks, and mobile communications often require processing of signals under simultaneous runtime, energy and/or power constraints. Hence, in addition to runtime, power and energy are first-order design considerations for...
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