We present compiler technology for generating sparse matrix code from (i) dense matrix code and (ii) a description of the indexing structure of the sparse matrices. This technology embeds statement instances into a Cartesian product of statement iteration and data spaces, and produces efficient sparse code by identifying common enumerations for multiple references to sparse matrices. This appro...

1 Robust Singular Spectrum Analysis for Erratic Noise Attenuation Ke Chen*, University of Alberta, Edmonton, Canada [email protected] and Mauricio D. Sacchi, University of Alberta, Edmonton, Canada [email protected] Summary The Singular Spectrum Analysis (SSA) method, also known as Cadzow filtering, adopts the truncated singular value decomposition (TSVD) or fast approximations to TSVD for rank...

Distributed dense word vectors have been shown to be effective at capturing tokenlevel semantic and syntactic regularities in language, while topic models can form interpretable representations over documents. In this work, we describe lda2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors. In contrast to continuous den...

Best linear unbiased prediction is well known for its wide range of applications including small area estimation. While the theory is well established for mixed linear models and under normality of the error and mixing distributions, the literature is sparse for nonlinear mixed models under nonnormality of the error or of the mixing distributions. This article develops a resampling based unifie...

We address the problem of efficient sparse fixed-rank (SFR) matrix decomposition, i.e., splitting a corrupted matrix M into an uncorrupted matrix L of rank r and a sparse matrix of outliers S. Fixed-rank constraints are usually imposed by the physical restrictions of the system under study. Here we propose a method to perform accurate and very efficient S-FR decomposition that is more suitable ...