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Empirical Bayes Flow Matching for Continuous Cryo-EM Heterogeneity — arXiv:2611.05202

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05202
License (as archived): CC0-1.0
Archived: 2026-11-04T18:54:25.822Z
SHA-256 of the archived text: 08a5971a3a699fff9305e65d0afd5731b80524d255097a686fc4c6d01c3f83a5
Stable URL of this page: https://metraipolis.com/documento/dc_bb637aea7fc2

How to cite

Empirical Bayes Flow Matching for Continuous Cryo-EM Heterogeneity — arXiv:2611.05202 — arXiv (en), https://arxiv.org/abs/2611.05202 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb637aea7fc2 (Metraipolis archive dc_bb637aea7fc2, sha256 08a5971a3a699fff9305e65d0afd5731b80524d255097a686fc4c6d01c3f83a5)

BibTeX

@misc{dcbb637aea7fc2,
  title = {Empirical Bayes Flow Matching for Continuous Cryo-EM Heterogeneity — arXiv:2611.05202},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05202},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb637aea7fc2; sha256 08a5971a3a699fff9305e65d0afd5731b80524d255097a686fc4c6d01c3f83a5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05202. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We address the problem of learning generative priors over high-dimensional latent variables from indirect, noisy observations, with a focus on continuous heterogeneity in cryo-electron microscopy (cryo-EM). In this setting, 3D atomic structures $x \in \mathbb{R}^{3N_a}$ are never directly observed and must be inferred from 2D projection images $y$ generated by a complex, non-linear forward operator. We propose Empirical Bayes Flow Matching (EB-FM), a method that learns a flow matching prior $p_\theta(x)$ directly from observations using a Monte Carlo stochastic approximation Expectation-Maximization framework. EB-FM alternates between an E-step that performs approximate posterior sampling from $p_\theta(x \mid y)$ via guided flow-based generation while maintaining persistent latent estimates through stochastic approximation, and an M-step that updates the flow model using these stabilized latent variables as training targets. This approach eliminates the need for pretraining on clean data and naturally accommodates non-linear forward models. We validate our method on controlled inverse problems using two-moons and MNIST data, and demonstrate its effectiveness on simulated cryo-EM data with known poses, showing that EB-FM can recover continuous distributions of protein conformations directly in atomic-coordinate space.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Biomolecules (q-bio.BM).
Pagina del lavoro: https://arxiv.org/abs/2611.05202 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T18:35:37.486Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).