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A Sticky HDP-HMM With Application to Speaker Diarization
TLDR
An augmented HDP-HMM is described that provides effective control over the switching rate and makes it possible to treat emission distributions nonparametrically, and a sampling algorithm is developed that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence.
An HDP-HMM for systems with state persistence
TLDR
A sampling algorithm is developed that employs a truncated approximation of the DP to jointly resample the full state sequence, greatly improving mixing rates and demonstrating the advantages of the sticky extension, and the utility of the HDP-HMM in real-world applications.
Nonparametric belief propagation
TLDR
The NBP algorithm is applied to infer component interrelationships in a parts-based face model, allowing location and reconstruction of occluded features and extends particle filtering methods to the more general vision problems that graphical models can describe.
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
TLDR
This work develops a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences in an unknown number of persistent, smooth dynamical modes.
Learning hierarchical models of scenes, objects, and parts
TLDR
Applied to a database of images of isolated objects, the sharing of parts among objects improves detection accuracy when few training examples are available and this hierarchical probabilistic model is extended to scenes containing multiple objects.
Nonparametric belief propagation
TLDR
This work describes an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models and illustrates the power of the resulting nonparametric BP algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks.
Bayesian Nonparametric Inference of Switching Dynamic Linear Models
TLDR
A sampling algorithm is developed that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order.
Graphical models for visual object recognition and tracking
TLDR
The approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints by building integrated scene models, which may discover contextual relationships, and better exploit partially labeled training images.
Sharing Features among Dynamical Systems with Beta Processes
TLDR
This work develops an efficient Markov chain Monte Carlo inference method that is based on the Indian buffet process representation of the predictive distribution of the beta process, and uses the sum-product algorithm to efficiently compute Metropolis-Hastings acceptance probabilities.
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