Probabilistic Forecasting with Stochastic Interpolants and F\" ollmer Processes

[PDF] arxiv.org

A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data

N Sabti, R Reddy, JB Muñoz, S Mishra-Sharma… - arXiv preprint arXiv …, 2024 - arxiv.org
Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far
stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped …
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[PDF] arxiv.org

Plug-in estimation of Schr\" odinger bridges

AA Pooladian, J Niles-Weed - arXiv preprint arXiv:2408.11686, 2024 - arxiv.org
We propose a procedure for estimating the Schr\" odinger bridge between two probability
distributions. Unlike existing approaches, our method does not require iteratively simulating …
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[PDF] arxiv.org

Flow Map Matching

NM Boffi, MS Albergo, E Vanden-Eijnden - arXiv preprint arXiv …, 2024 - arxiv.org
Generative models based on dynamical transport of measure, such as diffusion models, flow
matching models, and stochastic interpolants, learn an ordinary or stochastic differential …
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