SSLS combines score-based Langevin Monte Carlo with annealing for nonlinear posterior updates in sequential assimilation, supported by total-variation convergence bounds that establish asymptotic stability and numerical tests in high-dimensional nonlinear settings.
Posterior sampling via L angevin dynamics based on generative priors, 2024
2 Pith papers cite this work. Polarity classification is still indexing.
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A new method for nodal clustering in attributed graphs that combines low-dimensional node priors, a neural decoder, and graph-fused LASSO regularization on prior means, demonstrated via simulations on grid graphs and real data applications.
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Nonlinear Assimilation via Score-based Sequential Langevin Sampling
SSLS combines score-based Langevin Monte Carlo with annealing for nonlinear posterior updates in sequential assimilation, supported by total-variation convergence bounds that establish asymptotic stability and numerical tests in high-dimensional nonlinear settings.
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Decoder-only Clustering in Attributed Graphs
A new method for nodal clustering in attributed graphs that combines low-dimensional node priors, a neural decoder, and graph-fused LASSO regularization on prior means, demonstrated via simulations on grid graphs and real data applications.