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Researchers introduce online generalised predictive coding for dynamic inference and learning

This paper introduces Online Generalised Predictive Coding (ODEM), an extension of generalised filtering for real-time applications. ODEM enables simultaneous inference of latent states, learning of model parameters, and estimation of uncertainty. The method is demonstrated to effectively track latent states in complex, non-linear generative models, offering a biologically inspired approach to inference and learning in dynamic environments. AI

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IMPACT Introduces a biologically inspired framework for online inference and learning, potentially impacting how dynamic systems are modeled and understood.

RANK_REASON This is a research paper published on arXiv detailing a new methodology for online inference and learning.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Mehran H. Z. Bazargani, Szymon Urbas, Adeel Razi, Thomas Brendan Murphy, Karl Friston ·

    Online Generalised Predictive Coding

    arXiv:2605.02675v1 Announce Type: new Abstract: This paper introduces an extension of generalised filtering for online applications. Generalised filtering refers to data assimilation schemes that jointly infer latent states, learn unknown model parameters, and estimate uncertaint…

  2. arXiv stat.ML TIER_1 · Karl Friston ·

    Online Generalised Predictive Coding

    This paper introduces an extension of generalised filtering for online applications. Generalised filtering refers to data assimilation schemes that jointly infer latent states, learn unknown model parameters, and estimate uncertainty in an integrated framework -- e.g., estimate s…