We describe a novel max-margin parameter learning approach for structured prediction problems under certain non-decomposable performance measures. Structured prediction is a commo...
Background: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), i...
Peter D. Wentzell, Tobias K. Karakach, Sushmita Ro...
— We present a probabilistic framework for visual correspondence, inertial measurements and Egomotion. First, we describe a simple method based on Gabor filters to produce corre...
Abstract. Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Ny...
The goal of this research is to develop performance profiles of parallel and distributed applications in order to predict their execution time under different network conditions....