Deep Probabilistic Graphical Modeling
Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.
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Distributional Probabilistic Model Checking
Probabilistic model checking can provide formal guarantees on the behavior of stochastic models relating to a wide range of quantitative properties, such as runtime, energy consumption or cost. But decision making is typically with respect to the expected value of these quantities, which can mask important aspects of the full probability distribution ...
Ingy Elsayed-Aly, David Parker, Lu Feng
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A Probabilistic Model of Human Activity Recognition with Loose Clothing. [PDF]
Shen T, Di Giulio I, Howard M.
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Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model. [PDF]
Peng W +5 more
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LinguaPhylo: A probabilistic model specification language for reproducible phylogenetic analyses. [PDF]
Drummond AJ, Chen K, Mendes FK, Xie D.
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Egosyntonicity and emotion regulation: a probabilistic model of valence dynamics. [PDF]
Vitanza E, Mocenni C, De Lellis P.
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Few-shot CBCT-based synthetic CT generation with denoising diffusion probabilistic model. [PDF]
Yeap PL +5 more
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Diffusion probabilistic model for Tibetan painted sketch extraction. [PDF]
Wang F, Geng S, Jia Z, Dang M.
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Probabilistic model checking of cancer metabolism. [PDF]
Friedenberg MD +4 more
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Estimating future variant Creutzfeldt-Jakob disease cases in the UK: a cohort-based probabilistic model. [PDF]
Roberts B +8 more
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