Sparse Matrix Belief Propagation

Simple item page
dc.contributor.authorBixler, Reid Morrisen
dc.contributor.committeechairHuang, Berten
dc.contributor.committeememberWang, Gang Alanen
dc.contributor.committeememberHuang, Jia-Binen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2018-05-12T08:00:24Zen
dc.date.available2018-05-12T08:00:24Zen
dc.date.issued2018-05-11en
dc.description.abstractWe propose sparse-matrix belief propagation, which executes loopy belief propagation in Markov random fields by replacing indexing over graph neighborhoods with sparse-matrix operations. This abstraction allows for seamless integration with optimized sparse linear algebra libraries, including those that perform matrix and tensor operations on modern hardware such as graphical processing units (GPUs). The sparse-matrix abstraction allows the implementation of belief propagation in a high-level language (e.g., Python) that is also able to leverage the power of GPU parallelization. We demonstrate sparse-matrix belief propagation by implementing it in a modern deep learning framework (PyTorch), measuring the resulting massive improvement in running time, and facilitating future integration into deep learning models.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:15270en
dc.identifier.urihttp://hdl.handle.net/10919/83228en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectbelief propagationen
dc.subjectinferenceen
dc.subjectGPUen
dc.subjectsparse matrixen
dc.titleSparse Matrix Belief Propagationen
dc.typeThesisen
thesis.degree.disciplineComputer Science and Applicationsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Bixler_RM_T_2018.pdf
Size:
482.6 KB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses