Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Future systems will have to support multiple and concurrent dynamic compute-intensive applications, while respecting real-time and energy consumption constraints. To overcome these...
Nicolas Ventroux, Tanguy Sassolas, Raphael David, ...
This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in ...
Ahmed M. Elgammal, Vinay D. Shet, Yaser Yacoob, La...
We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as "unusually densely kni...
Chayant Tantipathananandh, Tanya Y. Berger-Wolf, D...
We study the dynamic membership problem, one of the most fundamental data structure problems, in the cell probe model with an arbitrary cell size. We consider a cell probe model e...