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Found 3 papers, 1 papers with code
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
We build a new class of generative algorithms capable of efficiently learning an arbitrary target distribution from possibly scarce, high-dimensional data and subsequently generate new samples.
Model Uncertainty and Correctability for Directed Graphical Models
Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence.
Uncertainty quantification for Markov Random Fields
In the latter, we develop uncertainty quantification bounds for finite size effects and phase diagrams, which constitute two of the typical predictions goals of statistical mechanics modeling.
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