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
arXiv:2607. 06079v1 Announce Type: new Abstract: Intelligent systems should not only solve tasks but also adapt under real-world constraints.
By Taiki Yamada, Kantaro Fujiwara
arXiv:2412. 12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm shift with the advent of learning-based methods.
By Arunabh Singh, Joyjit Mukherjee
arXiv:2511. 15409v2 Announce Type: replace Abstract: We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space models.
By Hany Abdulsamad, \'Angel F. Garc\'ia-Fern\'andez, Simo S\"arkk\"a
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.
By Matteo Rufolo, Dario Piga, Marco Forgione
arXiv:2608. 13215v1 Announce Type: new Abstract: Forecasting the long-horizon evolution of mechanical systems from position-only observations is a pivotal yet difficult task, as hidden velocities and trajectory-specific physical properties must be inferred simultaneously.
By Tianshuo Zhang, Xianglei Xing, Wenzhe Zhai, Jia Gao, He Cao