arxiv
PublishedOctober 21, 2026 at 4:00 AM
Meta-iLaD: Identifiable Latent Dynamics via Meta-Learning of Dynamics Environments
Publisher summary· verbatim
arXiv:2610.27180v1 Announce Type: new Abstract: Learning latent dynamics is central to assessing the current states and forecasting the future trajectories of high-dimensional time series. For locally-stationary latent dynamics $\mathcal{F}(\mathbf{z}_{<t}; \mathbf{c})$ with latent dynamics state $\
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Originally published on arxiv ↗