We propose novel algorithms for the detection, segmentation, recognition, and pose estimation of threedimensional objects. Our approach initially infers geometric primitives to de...
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
We develop a new framework for inferring models of transcriptional regulation. The models in this approach, which we call physical models, are constructed on the basis of verifiab...
Abstract—A fast offset surface generation approach is presented in this paper to construct intersection-free offset surfaces, which preserve sharp features, from freeform triangu...
We present a technique for analyzing a simulation metamodel that has been constructed using a variancestabilizing transformation. To compute a valid confidence interval for the ex...
Maria de los A. Irizarry, Michael E. Kuhl, Emily K...