Abstract. We consider the problem of sequence prediction in a probabilistic setting. Let there be given a class C of stochastic processes (probability measures on the set of one-wa...
A central theme of computational vision research has been the realization that reliable estimation of local scene properties requires propagating measurements across the image. Ma...
The surface estimation problem is used as a model to demonstrate a framework for solving early vision problems by high-order regularization with natural boundary conditions. Becaus...
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
—A recent paper [1] proposed a provably optimal polynomial time method for performing near-isometric point pattern matching by means of exact probabilistic inference in a chordal...