We propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are d...
We propose a probabilistic graphical model to represent weakly annotated images1 . This model is used to classify images and automatically extend existing annotations to new image...
This paper presents a novel approach to model the complex motion of human using a probabilistic autoregressive moving average model. The parameters of the model are adaptively tun...
Mohammad Hossein Ghaeminia, Amir Hossein Shabani, ...
Color-based tracking methods have proved to be efficient for their robustness qualities. The drawback of such global representation of an object is the lack of information on its s...
We present an unsupervised model for joint phrase alignment and extraction using nonparametric Bayesian methods and inversion transduction grammars (ITGs). The key contribution is...
Graham Neubig, Taro Watanabe, Eiichiro Sumita, Shi...