Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the ...
In this paper we introduce a novel architecture for data processing, based on a functional fusion between a data and a computation layer. We show how such an architecture can be le...
Radu Sion, Ramesh Natarajan, Inderpal Narang, Wen-...
In this paper, we develop multilingual supervised latent Dirichlet allocation (MLSLDA), a probabilistic generative model that allows insights gleaned from one language's data...
Animating 3D faces to achieve compelling realism is a challenging task in the entertainment industry. Previously proposed face transfer approaches generally require a high-quality...
TCP throughput prediction is an important capability in wide area overlay and multi-homed networks where multiple paths may exist between data sources and receivers. In this paper...
Mariyam Mirza, Joel Sommers, Paul Barford, Xiaojin...