A framework learns effective multiscale stochastic dynamics from single slow-variable paths by parameterizing the fast process invariant distribution with normalizing flows, trained end-to-end via penalized likelihood from stochastic averaging.
citation dossier
On the capacity of deep generative networks for approximating distributions.Neural networks, 145:144–154
1Pith papers citing it
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stat.MLtop field · 1 papers
UNVERDICTEDtop verdict bucket · 1 papers
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Pith has found this work in 1 reviewed paper. Its strongest current cluster is stat.ML (1 papers). The largest review-status bucket among citing papers is UNVERDICTED (1 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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stat.ML 1years
2026 1verdicts
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Learning stochastic multiscale models through normalizing flows
A framework learns effective multiscale stochastic dynamics from single slow-variable paths by parameterizing the fast process invariant distribution with normalizing flows, trained end-to-end via penalized likelihood from stochastic averaging.