A class of time-varying deep state-space model neural networks is proposed that learns dynamics via a dictionary of basis functions evolving differently over time, outperforming time-invariant versions on switching synthetic data and speech denoising.
arXiv preprint arXiv:2503.11627 , year=
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Time-Varying Deep State Space Models for Sequences with Switching Dynamics
A class of time-varying deep state-space model neural networks is proposed that learns dynamics via a dictionary of basis functions evolving differently over time, outperforming time-invariant versions on switching synthetic data and speech denoising.