arXiv AI July 22, 2026 By Matteo Rufolo, Dario Piga, Marco Forgione

Variational meta-learning inference for low dimensional neural system identification

arXiv:2607. 18965v1 Announce Type: cross Abstract: 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 reliable uncertainty quantification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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arXiv Machine Learning

Jun 25

Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach

arXiv:2606. 24966v1 Announce Type: new Abstract: Estimating parameters of dynamical systems from sparse, noisy, and irregularly sampled data is often severely ill-conditioned.

By Cristian Brugnara, Lea Multerer, Marco Forgione, Laura Azzimonti

arXiv Machine Learning

Sep 22

PAC-Bayesian Meta-Learning for Few-Shot Identification of Linear Dynamical Systems

arXiv:2609.24117v1 Announce Type: new Abstract: Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system id...

By Chenfeng Huang, George Michailidis

arXiv Machine Learning

Aug 31

Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture - Bridging Predictive and Generative Self-Supervised Learning

arXiv:2603.20111v2 Announce Type: replace Abstract: The Joint-Embedding Predictive Architecture (JEPA) is often seen as a non-generative alternative to likelihood-based self-supervised learning, emph...

By Moritz G\"ogl, Christopher Yau

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arXiv Machine Learning

Jun 16

The Data Manifold under the Microscope

arXiv:2606. 15760v1 Announce Type: new Abstract: A significant gap exists between theory and practice in deep learning.

By Marios Koulakis, Constantin Seibold

benchmarks

arXiv AI

Jun 2

Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics

arXiv:2606. 01468v1 Announce Type: cross Abstract: Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dynamical latent variable modeling of single-cell neural recordings.

By JR Huml, Jonathan Wenger, John P. Cunningham

arXiv Machine Learning

Aug 13

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

arXiv:2608. 11435v1 Announce Type: new Abstract: Forward and inverse modeling of parametric dynamical systems requires surrogate models that are not only accurate for state prediction, but also informative for parameter calibration.

By Qiyao Zhou, Xujia Zhu, Pierre Joli, Yu Cong, Sibo Cheng

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Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach

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Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture - Bridging Predictive and Generative Self-Supervised Learning

The Data Manifold under the Microscope

Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates