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New paper details approximating measures on function spaces for generative modeling

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A new paper on arXiv introduces the class $\mathcal{P}_\psi(\mu)$ of measures that approximate reference measures $\mu$ through a finite-dimensional map $\psi$. This approach is particularly useful for Bayesian inverse problems and generative modeling, allowing for efficient sampling and representation. The research demonstrates applications in areas such as nonlinear observation maps, deconvolution with a jump process prior, and state estimation for Navier-Stokes flows. AI

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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New paper details approximating measures on function spaces for generative modeling

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ricardo Baptista, Bamdad Hosseini, Alexander W. Hsu ·

    Approximating Measures on Function Spaces: Transport and Truncation

    arXiv:2609.17802v1 Announce Type: cross Abstract: Measures on function spaces arise throughout Bayesian inverse problems and generative modeling, often with low-dimensional structure relative to a tractable reference measure. We introduce the class $\mathcal{P}_\psi(\mu)$ of meas…

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