  We observe that the standard log likelihood training objective for a
Recurrent Neural Network (RNN) model of time series data is equivalent to a
variational Bayesian training objective, given the proper choice of generative
and inference models. This perspective may motivate extensions to both RNNs and
variational Bayesian models. We propose one such extension, where multiple
particles are used for the hidden state of an RNN, allowing a natural
representation of uncertainty or multimodality.
