One of the two existing strategies of resolving singularities of multifold Mellin–Barnes integrals in the dimensional regularization parameter, or a parameter of the analytic regularization, is formulated in a modified form. The corresponding algorithm is implemented as a Mathematica code MBresolve.m E-mail: [email protected] E-mail: [email protected]

An effective approach to structure learning and parameter estimation for Gaussian graphical models is to impose a sparsity prior, such as a Laplace prior, on the entries of the precision matrix. Such an approach involves a hyperparameter that must be tuned to control the amount of sparsity. In this paper, we introduce a parameter-free method for estimating a precision matrix with sparsity that ...

While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data. In this work we introduce a suite of tools that exploit sparsity in both the feature maps and the filter weights, and thereby allow for significantly lower memory footprints and computation times than the conventional dense fram...

Restricted BoltzmannMachine (RBM) has been applied to a wide variety of tasks due to its advantage in feature extraction. Implementing sparsity constraint in the activated hidden units is an important improvement on RBM. The sparsity constraints in the existing methods are usually specified by users and are independent of the input data. However, the input data could be heterogeneous in content...

Expensive feature engineering based on WordNet senses has been shown to be useful for document level sentiment classification. A plausible reason for such a performance improvement is the reduction in data sparsity. However, such a reduction could be achieved with a lesser effort through the means of syntagma based word clustering. In this paper, the problem of data sparsity in sentiment analys...

In this paper, we exploit the notion of partial sparsity for scene reconstruction associated with through-the-wall radar imaging of stationary targets under reduced data volume. Partial sparsity implies that the scene being imaged consists of a sparse part and a dense part, with the support of the latter assumed to be known. For the problem at hand, sparsity is represented by a few stationary i...

In this paper, we focus on learning product graphs from multi-domain data. We assume that the graph is formed by Cartesian of two smaller graphs, which refer to as factors. pose problem estimating factor Laplacian matrices. To capture local interactions in data, seek sparse factors and a smoothness model for propose an efficient iterative solver then extend infer multi-component with applicatio...