How can we efficiently propagate uncertainty in a latent state representation
with recurrent neural networks? This paper introduces stochastic recurrent
neural networks which glue a deterministic recurrent neural network and a state
space model together to form a stochastic and sequential neural generative
model. The clear separation of deterministic and stochastic layers allows a
structured variational inference network to track the factorization of the
model's posterior distribution. By retaining both the nonlinear recursive
structure of a recurrent neural network and averaging over the uncertainty in a
latent path, like a state space model, we improve the state of the art results
on the Blizzard and TIMIT speech modeling data sets by a large margin, while
achieving comparable performances to competing methods on polyphonic music
modeling.

本文介绍了一种新的神经网络模型 —— 随机循环神经网络，该模型能高效地传播潜在状态的不确定性来构建序列神经生成模型，并在语音和音乐建模方面取得了比同类方法更好的结果。