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Nonparametric belief propagation for self-localization of sensor networks
TLDR
It is demonstrated that the information used for sensor localization is fundamentally local with regard to the network topology and used to reformulate the problem within a graphical model framework, and that judicious message construction can result in better estimates.
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.
Variational Inference for Crowdsourcing
TLDR
By choosing the prior properly, both BP and MF perform surprisingly well on both simulated and real-world datasets, competitive with state-of-the-art algorithms based on more complicated modeling assumptions.
Fast collapsed gibbs sampling for latent dirichlet allocation
TLDR
A novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model, which can be as much as 8 times faster than the standard collapsed Gibbs sampler for LDA and results in significant speedups on real world text corpora.
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.
Particle Belief Propagation
TLDR
This paper describes a generic particle belief propagation (PBP) algorithm which is closely related to previously proposed methods and proves that this algorithm is consistent, approaching the true BP messages as the number of samples grows large.
Loopy Belief Propagation: Convergence and Effects of Message Errors
TLDR
This analysis leads to convergence conditions for traditional BP message Passing, and both strict bounds and estimates of the resulting error in systems of approximate BP message passing.
Adaptive event detection with time-varying poisson processes
TLDR
The experimental results indicate that the proposed time-varying Poisson model provides a robust and accurate framework for adaptively and autonomously learning how to separate unusual bursty events from traces of normal human activity.
Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis
TLDR
It is speculated that in humans the circadian clock imposes regulation of epidermal cell proliferation so that skin is at a particularly vulnerable stage during times of maximum UV exposure, thus contributing to the high incidence of human skin cancers.
Nonparametric belief propagation for self-calibration in sensor networks
TLDR
It is demonstrated that the information used for sensor calibration is fundamentally local with regard to the network topology and the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties is demonstrated.
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