  Sequential data often originates from diverse domains across which
statistical regularities and domain specifics exist. To specifically learn
cross-domain sequence representations, we introduce disentangled state space
models (DSSM) -- a class of SSM in which domain-invariant state dynamics is
explicitly disentangled from domain-specific information governing that
dynamics. We analyze how such separation can improve knowledge transfer to new
domains, and enable robust prediction, sequence manipulation and domain
characterization. We furthermore propose an unsupervised VAE-based training
procedure to implement DSSM in form of Bayesian filters. In our experiments, we
applied VAE-DSSM framework to achieve competitive performance in online ODE
system identification and regression across experimental settings, and
controlled generation and prediction of bouncing ball video sequences across
varying gravitational influences.
