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Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Practitioners typically assume that the latent space is semantically meaningful. It is used to check models, summarize the corpus, and guide exploration of its contents.(More)
We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel (MMSB). It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world(More)
Before contributing new knowledge, individuals must attain requisite background knowledge or skills through schooling, training, practice, and experience. Given limited time, individuals often choose either to focus on few areas, where they build deep expertise, or to delve less deeply and distribute their attention and efforts across several areas. In this(More)
We give two new criteria by which pairs of permutations may be compared in defining the Bruhat order (of type A). One criterion uses totally nonnegative polynomials and the other uses Schur functions. The Bruhat order on S n is often defined by comparing two permutations π = π(1) · · · π(n) and σ = σ(1) · · · σ(n) according to the following criterion: π ≤ σ(More)
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