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A Sticky HDP-HMM With Application to Speaker Diarization
tl;dr
We take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. Expand
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  • Open Access
An HDP-HMM for systems with state persistence
tl;dr
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. Expand
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Nonparametric belief propagation
tl;dr
This paper develops a nonparametric belief propagation (NBP) algorithm applicable to general graphs. Expand
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Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
tl;dr
We use a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes and thereby capture a wider range of temporal dependencies. Expand
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Learning hierarchical models of scenes, objects, and parts
tl;dr
We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. Expand
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Bayesian Nonparametric Inference of Switching Dynamic Linear Models
tl;dr
We use a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes to learn a switching linear dynamical system that switches among a set of conditionally linear modes. Expand
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Sharing Features among Dynamical Systems with Beta Processes
tl;dr
We propose a Bayesian nonparametric approach to the problem of modeling related time series. Expand
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Graphical models for visual object recognition and tracking
tl;dr
We develop statistical methods which allow effective visual detection, categorization, and tracking of objects in complex scenes. Expand
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Visual Hand Tracking Using Nonparametric Belief Propagation
tl;dr
This paper develops probabilistic methods for visual tracking of a three-dimensional geometric hand model from monocular image sequences using the recently proposed nonparametric belief propagation algorithm. Expand
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  • Open Access