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Sequential Neural Models with Stochastic Layers
Marco Fraccaro · Søren Kaae Sønderby · Ulrich Paquet · Ole Winther

Tue Dec 06 08:00 AM -- 08:20 AM (PST) @ Area 1 + 2

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.

Author Information

Marco Fraccaro (DTU)
Søren Kaae Sønderby (KU)
Ulrich Paquet (DeepMind)
Ole Winther (DTU)

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