Title: Collapsed amortized variational inference for switching nonlinear dynamical systems. Abstract: We propose an efficient inference method for switching nonlinear dynamical systems. The key idea is to learn an inference network which can be used as a proposal distribution for the continuous latent variables, while performing exact marginalization of the discrete latent variables. This allows us to use the reparameterization trick, and apply end-to-end training with SGD. We show that this method can successfully segment time series data (including videos) into meaningful "regimes", due to the use of piece-wise nonlinear dynamics.