arXiv (All)
open access
Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders
As AI models move toward clinical decision-making and personalized treatment, understanding \emph{what} a model learns is important beyond predictive accuracy alone. We investigate whether personalized latent dynamics reveal clinically associated differences even when predictive fit is similar. A li
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Machine Learning-Driven Risk Prediction Model in Transthyretin Amyloid Cardiomyopathy.#615738
- Individual-First Brain State Modeling Reconciles Personalized Prediction with Generalizable Mechanistic Interpretation in Precision Psychiatry#660931
- GeneICL: A Tabular Foundation Model for Bulk Transcriptomics#1218486
- GeneICL: A Tabular Foundation Model for Bulk Transcriptomics#1210290
- Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering#651825
Record · ID 1247777
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive