Probabilistic sensor models defined as dynamic Bayesian networks can possess an inherent sparsity that is not reflected in the structure of the network. Classical inference algorithms like variable elimination and junction tree propagation cannot exploit this sparsity. Also, they do not exploit the opportunities for sharing calculations among different time slices of the model. We show that, us...

This study develops a data-driven group variable selection method for data envelopment analysis (DEA), a non-parametric linear programming approach to the estimation of production frontiers. The proposed method extends the group Lasso (least absolute shrinkage and selection operator) designed for variable selection on (often predefined) groups of variables in linear regression models to DEA mod...

We derive a new parallel communication-avoiding matrix powers algorithm for matrices of the form A = D + USV H , where D is sparse and USV H has low rank and is possibly dense. We demonstrate that, with respect to the cost of computing k sparse matrix-vector multiplications, our algorithm asymptotically reduces the parallel latency by a factor of O(k) for small additional bandwidth and computat...

Vector valued data appearing in concrete applications often possess sparse expansions with respect to a preassigned frame for each vector component individually. Additionally, different components may also exhibit common sparsity patterns. Recently, there were introduced sparsity measures that take into account such joint sparsity patterns, promoting coupling of non-vanishing components. These ...

This work investigates three penalized-likelihood expectation maximization (EM) algorithms for image reconstruction with Poisson data where the images are known a priori to be sparse in the space domain. The penalty functions considered are the 1 norm, the 0 “norm,” and a penalty function based on the sum of logarithms of pixel values, R(x) = ∑np j=1 log (xj δ + 1 ) . Our results show that the ...

With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques – collaborative filtering. Apart ...

Feature selection has evolved to be a very important step in several machine learning paradigms. Especially in the domains of bio-informatics and text classification which involve data of high dimensions, feature selection can help in drastically reducing the feature space. In cases where it is difficult or infeasible to obtain sufficient training examples, feature selection helps overcome the ...

Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural at...

In this report, we present the literature survey done for our work with SA and other NLP applications. The road map of this report is as follows. In Section-1, we introduce clustering process and describe a few existing word clustering techniques. Section-2 talks about the smoothing process followed by why clustering is better for our work in Section-3. Finally in Section-4, we talk about the r...