9,471 research outputs found
An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process
Stochastic variational inference (SVI) is emerging as the most promising
candidate for scaling inference in Bayesian probabilistic models to large
datasets. However, the performance of these methods has been assessed primarily
in the context of Bayesian topic models, particularly latent Dirichlet
allocation (LDA). Deriving several new algorithms, and using synthetic, image
and genomic datasets, we investigate whether the understanding gleaned from LDA
applies in the setting of sparse latent factor models, specifically beta
process factor analysis (BPFA). We demonstrate that the big picture is
consistent: using Gibbs sampling within SVI to maintain certain posterior
dependencies is extremely effective. However, we find that different posterior
dependencies are important in BPFA relative to LDA. Particularly,
approximations able to model intra-local variable dependence perform best.Comment: ICML, 12 pages. Volume 37: Proceedings of The 32nd International
Conference on Machine Learning, 201
Analytic Properties and Covariance Functions of a New Class of Generalized Gibbs Random Fields
Spartan Spatial Random Fields (SSRFs) are generalized Gibbs random fields,
equipped with a coarse-graining kernel that acts as a low-pass filter for the
fluctuations. SSRFs are defined by means of physically motivated spatial
interactions and a small set of free parameters (interaction couplings). This
paper focuses on the FGC-SSRF model, which is defined on the Euclidean space
by means of interactions proportional to the squares of the
field realizations, as well as their gradient and curvature. The permissibility
criteria of FGC-SSRFs are extended by considering the impact of a
finite-bandwidth kernel. It is proved that the FGC-SSRFs are almost surely
differentiable in the case of finite bandwidth. Asymptotic explicit expressions
for the Spartan covariance function are derived for and ; both known
and new covariance functions are obtained depending on the value of the
FGC-SSRF shape parameter. Nonlinear dependence of the covariance integral scale
on the FGC-SSRF characteristic length is established, and it is shown that the
relation becomes linear asymptotically. The results presented in this paper are
useful in random field parameter inference, as well as in spatial interpolation
of irregularly-spaced samples.Comment: 24 pages; 4 figures Submitted for publication to IEEE Transactions on
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Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
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