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ELPAC: Endpoint-Anchored Latent Progression with Stage-Varying Multimodal Coordination — arXiv:2611.08541

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08541
License (as archived): CC0-1.0
Archived: 2026-11-09T22:41:47.355Z
SHA-256 of the archived text: 2f0d64885dd0888e41bc688d69098655cfb35f45510de43e287ee68a56d5eb72
Stable URL of this page: https://metraipolis.com/documento/dc_76e25d328706

How to cite

ELPAC: Endpoint-Anchored Latent Progression with Stage-Varying Multimodal Coordination — arXiv:2611.08541 — arXiv (en), https://arxiv.org/abs/2611.08541 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_76e25d328706 (Metraipolis archive dc_76e25d328706, sha256 2f0d64885dd0888e41bc688d69098655cfb35f45510de43e287ee68a56d5eb72)

BibTeX

@misc{dc76e25d328706,
  title = {ELPAC: Endpoint-Anchored Latent Progression with Stage-Varying Multimodal Coordination — arXiv:2611.08541},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08541},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_76e25d328706; sha256 2f0d64885dd0888e41bc688d69098655cfb35f45510de43e287ee68a56d5eb72},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08541. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Aging cohorts increasingly combine imaging, plasma, and cognitive measurements, but most subjects provide only a partially observed cross-sectional snapshot, leaving latent stage, progression subtype, and stage-varying multimodal coordination to be inferred jointly. Existing methods estimate the cohort-level aging process, model covariance under observed covariates, or fuse modalities for prediction. However, they rarely target the joint problem of endpoint-anchored staging and progression-resolved residual coordination across modalities, even though subject staging, subtype assignment, and multimodal interplay are mutually dependent. We introduce ELPAC, a Bayesian framework with two interlocking constructions. The first integrates subtype trajectories backward from clinically reliable endpoint groups along biologically structured velocity components encoding modality-specific monotone direction and optional sequential ordering priors. The second factorizes residual multimodal variation around the inferred stage axis into sparse coordination axes whose loadings and activations expose which multimodal patterns are operative in each subtype and when they emerge, with stage-resolved variation supplying the auxiliary structure for conditional identifiability. We use a scalable amortized variational inference scheme that enables joint posterior inference at cohort scale with single-pass deployment on new subjects. On synthetic data and a harmonized ADNI+NACC/SCAN Alzheimer's cohort, ELPAC demonstrates accurate latent-structure recovery, well-calibrated prediction intervals, and biologically interpretable multimodal coordination programs that differ by subtype.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2611.08541 — 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 2026-11-09T22:33:47.168Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).