• Login
    Search 
    •   Treasures Home
    • Electronic Theses and Dissertations
    • Search
    •   Treasures Home
    • Electronic Theses and Dissertations
    • Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Search

    Show Advanced FiltersHide Advanced Filters

    Filters

    Use filters to refine the search results.

    Now showing items 1-5 of 5

    • Sort Options:
    • Relevance
    • Title Asc
    • Title Desc
    • Issue Date Asc
    • Issue Date Desc
    • Results Per Page:
    • 5
    • 10
    • 20
    • 40
    • 60
    • 80
    • 100
    Thumbnail

    Global Variational Learning for Graphical Models with Latent Variables 

    Abdelatty, Ahmed M. (2018-05)
    Probabilistic Graphical Models have been used intensively for developing Machine Learning applications including Computer Vision, Natural Language processing, Collaborative Filtering, and Bioinformatics. Moreover, Graphical ...
    Thumbnail

    Advances in Message-Passing Algorithms in Propositional and Lifted Graphical Models 

    Smith, David B. (2017-05)
    With the profusion of data across new and complicated domains, the compactness and expressivity of PGMs have made them a cornerstone of modern, data-driven technologies. Unfortunately, PGMs suffer one substantial drawback; ...
    Thumbnail

    Scalable Learning Approaches for Sum-Product-Cutset Networks 

    Rahman, Tahrima (2016-12)
    Tractable models are a subclass of probabilistic graphical models (PGMs) in which exact inference can be performed tractably – a very desirable property missing from arbitrary PGMs like Bayesian and Markov networks – exact ...
    Thumbnail

    Parameter Tying and Dissociation in Graphical Models 

    Chou, Li Kang (2019-12)
    Understanding the implications of today’s deluge and high velocity of data is a challenging problem facing researchers across multiple disciplines and domains. Data are typically highdimensional, unstructured, and noisy; ...
    Thumbnail

    Variational Inference Methods for Continuous Probabilistic Graphical Models 

    Guo, Yuanzhen (2020-11-17)
    Graphical models provide a general framework for representing and reasoning about data. Once these models are fit to data, they can be used to answer statistical queries about the observed data. Unfortunately these answering ...

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of TreasuresCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CommunityBy Issue DateAuthorsTitlesSubjects

    My Account

    Login

    Discover

    AuthorAbdelatty, Ahmed M. (1)Chou, Li Kang (1)Guo, Yuanzhen (1)Rahman, Tahrima (1)Smith, David B. (1)Subject
    Graphical modeling (Statistics) (5)
    Machine learning (3)Inference (2)Computer networks--Scalability (1)Constrained optimization (1)Information theory (1)Latent variables (1)Nonconvex programming (1)Probabilistic databases (1)Set theory (1)... View MoreDate Issued2016 (1)2017 (1)2018 (1)2019 (1)2020 (1)Has File(s)Yes (5)

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV