Robust tracking of abrupt motion is a challenging task
in computer vision due to the large motion uncertainty. In
this paper, we propose a stochastic approximation Monte
Carlo (...
This paper addresses recognition of human activities with stochastic structure, characterized by variable spacetime arrangements of primitive actions, and conducted by a variable ...
This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
Abstract: Transactional network data can be thought of as a list of oneto-many communications (e.g., email) between nodes in a social network. Most social network models convert th...
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...