In this paper, we propose a novel supervised hierarchical sparse coding model based on local image descriptors for classification tasks. The supervised dictionary training is perf...
Until recently, parallel programming has largely focused on the exploitation of data-parallelism in dense matrix programs. However, many important application domains, including m...
Milind Kulkarni, Martin Burtscher, Calin Cascaval,...
Abstract. Hierarchical classification problems gained increasing attention within the machine learning community, and several methods for hierarchically structured taxonomies have...
Abstract. Clustering high dimensional data with sparse features is challenging because pairwise distances between data items are not informative in high dimensional space. To addre...
We consider the problem of e-Infrastructures that wish to reconcile the generality of their services with the bespoke requirements of diverse user communities. We motivate the requ...
Fabio Simeoni, Leonardo Candela, David Lievens, Pa...