In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of th...
This paper presents a sequential state estimation method with arbitrary probabilistic models expressing the system’s belief. Probabilistic models can be estimated by Maximum a po...
We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motio...
Shannon's Noisy-Channel model, which describes how a corrupted message might be reconstructed, has been the corner stone for much work in statistical language and speech proc...
This paper presents a novel probabilistic approach to fusing multimodal metadata for event based home photo clustering. Photo events are characterized by the coherence of multimod...
Tao Mei, Bin Wang, Xian-Sheng Hua, He-Qin Zhou, Sh...