From the 2 of 12 linked papers with an AI index.
12 papers
Joint State-Parameter Inference Enhances Estimation Performance in Model-Based Digital Therapeutics for Type 1 Diabetes
Milad Banitalebi Dehkordi, Vihangkumar V. Naik, Manas Mejari +2
The paper introduces a recursive filtering method that jointly estimates glucose levels and model parameters in real time for type‑1 diabetes, using a Rao‑Blackwellized Stein Varia…
Variational meta-learning inference for low dimensional neural system identification
Matteo Rufolo, Dario Piga, Marco Forgione
Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overfitting in low-data regimes and lack relia…
Learning reduced-order latent linear models for Kalman filtering of nonlinear systems
Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga
The paper introduces an end-to-end learning framework that jointly trains an autoencoder and a reduced-order linear model to perform Kalman filtering directly in a low-dimensional…
From system models to class models: An in-context learning paradigm
Marco Forgione, Filippo Pura, Dario Piga
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same cla…
ASIA: an Autonomous System Identification Agent
Dario Piga, Marco Forgione
Over the years, research in system identification has provided a rich set of methods for learning dynamical models, together with well-established theoretical guarantees. In practi…
Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation
Milad Banitalebi Dehkordi, Manas Mejari, Dario Piga
We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses Rao-Blackwellization techniqu…