  In this work, we propose a novel probabilistic sequence model that excels at
capturing high variability in time series data, both across sequences and
within an individual sequence. Our method uses temporal latent variables to
capture information about the underlying data pattern and dynamically decodes
the latent information into modifications of weights of the base decoder and
recurrent model. The efficacy of the proposed method is demonstrated on a range
of synthetic and real-world sequential data that exhibit large scale
variations, regime shifts, and complex dynamics.
