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Full-Text Articles in Physical Sciences and Mathematics
Scaling Mcmc Inference And Belief Propagation To Large, Dense Graphical Models, Sameer Singh
Scaling Mcmc Inference And Belief Propagation To Large, Dense Graphical Models, Sameer Singh
Doctoral Dissertations
With the physical constraints of semiconductor-based electronics becoming increasingly limiting in the past decade, single-core CPUs have given way to multi-core and distributed computing platforms. At the same time, access to large data collections is progressively becoming commonplace due to the lowering cost of storage and bandwidth. Traditional machine learning paradigms that have been designed to operate sequentially on single processor architectures seem destined to become obsolete in this world of multi-core, multi-node systems and massive data sets. Inference for graphical models is one such example for which most existing algorithms are sequential in nature and are difficult to scale …
Incorporating Boltzmann Machine Priors For Semantic Labeling In Images And Videos, Andrew Kae
Incorporating Boltzmann Machine Priors For Semantic Labeling In Images And Videos, Andrew Kae
Doctoral Dissertations
Semantic labeling is the task of assigning category labels to regions in an image. For example, a scene may consist of regions corresponding to categories such as sky, water, and ground, or parts of a face such as eyes, nose, and mouth. Semantic labeling is an important mid-level vision task for grouping and organizing image regions into coherent parts. Labeling these regions allows us to better understand the scene itself as well as properties of the objects in the scene, such as their parts, location, and interaction within the scene. Typical approaches for this task include the conditional random field …