arXiv AI June 2, 2026 By JR Huml, Jonathan Wenger, John P. Cunningham

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.

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.

arXiv Machine Learning

Jul 31

Latent Matters: Learning Deep State-Space Models

arXiv:2602. 23050v2 Announce Type: replace Abstract: Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data.

By Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt

benchmarks

arXiv Machine Learning

Jun 15

Uncertainty Estimation and Generalization Bounds for Modern Deep Learning

arXiv:2606. 13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems.

By Luis A. Ortega

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

benchmarks

arXiv Machine Learning

Jul 17

Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments

arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.

By Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee

llms efficiency

arXiv Machine Learning

Jun 2

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning

arXiv:2412. 04177v2 Announce Type: replace Abstract: Recently, there has been an increasing interest in performing post-hoc uncertainty estimation about the predictions of pre-trained deep neural networks (DNNs).

By Luis A. Ortega, Sim\'on Rodr\'iguez-Santana, Daniel Hern\'andez-Lobato

benchmarks

arXiv Machine Learning

Sep 10

Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction

arXiv:2609.08620v1 Announce Type: cross Abstract: We address the challenge of scalable uncertainty quantification in large-scale scientific applications, where complex state-of-the-art machine learni...

By Yousra El-Bachir, Frederik Van der Veken, Davide di Croce, Carlo Emilio Montanari, Massimo Giovannozzi, Ekaterina Krymova, Tatiana Pieloni

benchmarks

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Latent Matters: Learning Deep State-Space Models

Uncertainty Estimation and Generalization Bounds for Modern Deep Learning

Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates

Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments

Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning

Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction