Year: 2016
Type: conference-paper
Abstract: 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.
Authors: M. Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, Ole Winther
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Microsoft Research, Nvidia
Award: G5801408398
OpenAlex ID: W2963279312