Abstract. We present a framework for vision-assisted tagging of personal photo collections using context. Whereas previous efforts mainly focus on tagging people, we develop a uni...
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. State-space models of physical phenomena su...
Juan Liu, Maurice Chu, Jie Liu, Jim Reich, Feng Zh...
Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state a...
We present an approximate inference approach to parameter estimation in a spatio-temporal stochastic process of the reaction-diffusion type. The continuous space limit of an infer...