Distributed learning is a problem of fundamental interest in machine learning and cognitive science. In this paper, we present asynchronous distributed learning algorithms for two...
We present in this paper a novel object tracking system based on 3D contour models. For this purpose, we integrate two complimentary likelihoods, defined on local color statistics...
Abstract— We present a unifying framework for continuous optimization and sampling. This framework is based on Gaussian Adaptation (GaA), a search heuristic developed in the late...
This paper deals with the interaction between progressive model adaptation and score normalization strategies which are used for reducing the variation in likelihood ratio scores ...
When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with man...