Mashups integrate a set of Web-services and data sources, often referred to as mashlets. We study in this paper a common scenario where these mashlets are components of larger Web...
There are many academic and commercial stream processing engines (SPEs) today, each of them with its own execution semantics. This variation may lead to seemingly inexplicable diï...
Abstract. Semi-supervised clustering models, that incorporate user provided constraints to yield meaningful clusters, have recently become a popular area of research. In this paper...
In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selectio...
Abstract. Bayesian reinforcement learning (RL) is aimed at making more efï¬cient use of data samples, but typically uses signiï¬cantly more computation. For discrete Markov Decis...
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
—In the environments where heterogeneous devices need to share information, utilize services of each other, and participate as components in various smart applications, it is com...
—The goal of this paper is to correct bleed-through in degraded documents using a variational approach. The variational model is adapted using an estimated background according t...
—We propose a unified graphical model that can represent both the causal and noncausal relationships among random variables and apply it to the image segmentation problem. Specif...
—In this paper we present a compositional and dynamic model for face aging. The compositional model represents faces in each age group by a hierarchical And-Or graph, in which An...
Jin-Li Suo, Song Chun Zhu, Shiguang Shan, Xilin Ch...