Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among...
This paper studies the inference of 3D shape from a set of ? noisy photos. We derive a probabilistic framework to specify what one can infer about 3D shape for arbitrarily-shaped, ...
Rahul Bhotika, David J. Fleet, Kiriakos N. Kutulak...
We study the dynamic membership problem, one of the most fundamental data structure problems, in the cell probe model with an arbitrary cell size. We consider a cell probe model e...
In this paper we show how Constraint Programming (CP) techniques can improve the efficiency and applicability of grid-based algorithms for optimising surface contact between comple...
Motivated by an application in computational topology, we consider a novel variant of the problem of efficiently maintaining dynamic rooted trees. This variant allows an operation...