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There has been an explosion in the amount of digital text information available in recent years, leading to challenges of scale for traditional inference algorithms for topic models. Recent advances in stochastic variational inference algorithms for latent Dirichlet allocation (LDA) have made it feasible to learn topic models on very large-scale corpora,(More)
Non-Negative Matrix Factorization (NMF) is a dimensionality reduction method that has been shown to be very useful for a variety of tasks in machine learning and data mining. One of the fastest algorithms for NMF is the Block Principal Pivoting method (BPP) of [6], which follows a block coordinate descent approach. The optimization in each iteration(More)
Learning problems, such as logistic regression, are typically formulated as pure optimization problems defined on some loss function. We argue that this view ignores the fact that the loss function depends on stochastically generated data which in turn determines an intrinsic scale of precision for statistical estimation. By considering the statistical(More)
We present a scalable sequential Monte Carlo algorithm and its greedy counterpart for models based on Kingman’s coalescent. We utilize fast nearest neighbor algorithms to limit expensive computations to only a subset of data point pairs. For a dataset size of n, the resulting algorithm has O(n log n) computational complexity. We empirically verify that we(More)
A gas chromatographic on-column methylation technique was developed for the routine laboratory determination of 5-(p-hydroxyphenyl)-5-phenylhydantoin (p-HPPH), the principal urinary product of phenytoin )PHT) metabolism in man. 5-(p-Hydroxyphenyl)-5-(p-tolyl)hydantoin (HMPPH), a new internal standard, was synthesized and evaluated against(More)
An on-column methylation technique (OCMT) is described for the simultaneous, gas chromatographic determination in blood of ethosuximide (ES), phenobarbital (PB), primidone (PD), phenytoin (DPH), and 5-ethyl-5-phenylhydantoin (EPH). Multiple internal standards are employed in the OCMT, in order to eliminate or to minimize greatly error sources common to the(More)
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