We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
Abstract. We present an alignment framework for object detection using a hierarchy of 3D polygonal models. One difficulty with alignment methods is that the high-dimensional transf...
Abstract—Optimizing compilers apply numerous interdependent optimizations, leading to the notoriously difficult phase-ordering problem — that of deciding which transformations...
Konrad Trifunovic, Dorit Nuzman, Albert Cohen, Aya...
Complexity, or in other words compactness, of models generated by rule learners is one of often neglected issues, although it has a profound effect on the success of any project t...
Dynamically allocating computing nodes to parallel applications is a promising technique for improving the utilization of cluster resources. Detailed simulations can help identify...
Basile Schaeli, Sebastian Gerlach, Roger D. Hersch