Sequential neural models with stochastic layers

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Sequential neural models with stochastic layers. / Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich; Winther, Ole.

Neural Information Processing Systems 2016. ed. / D. D. Lee; M. Sugiyama; U. V. Luxburg; I. Guyon; R. Garnett. Neural Information Processing Systems Foundation, 2016. p. 2207-2215 (Advances in Neural Information Processing Systems, Vol. 29).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Fraccaro, M, Sønderby, SK, Paquet, U & Winther, O 2016, Sequential neural models with stochastic layers. in DD Lee, M Sugiyama, UV Luxburg, I Guyon & R Garnett (eds), Neural Information Processing Systems 2016. Neural Information Processing Systems Foundation, Advances in Neural Information Processing Systems, vol. 29, pp. 2207-2215, 30th Annual Conference on Neural Information Processing Systems, Barcelona, Spain, 05/12/2016. <https://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf>

APA

Fraccaro, M., Sønderby, S. K., Paquet, U., & Winther, O. (2016). Sequential neural models with stochastic layers. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, & R. Garnett (Eds.), Neural Information Processing Systems 2016 (pp. 2207-2215). Neural Information Processing Systems Foundation. Advances in Neural Information Processing Systems Vol. 29 https://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf

Vancouver

Fraccaro M, Sønderby SK, Paquet U, Winther O. Sequential neural models with stochastic layers. In Lee DD, Sugiyama M, Luxburg UV, Guyon I, Garnett R, editors, Neural Information Processing Systems 2016. Neural Information Processing Systems Foundation. 2016. p. 2207-2215. (Advances in Neural Information Processing Systems, Vol. 29).

Author

Fraccaro, Marco ; Sønderby, Søren Kaae ; Paquet, Ulrich ; Winther, Ole. / Sequential neural models with stochastic layers. Neural Information Processing Systems 2016. editor / D. D. Lee ; M. Sugiyama ; U. V. Luxburg ; I. Guyon ; R. Garnett. Neural Information Processing Systems Foundation, 2016. pp. 2207-2215 (Advances in Neural Information Processing Systems, Vol. 29).

Bibtex

@inproceedings{8a9e65c4da6f402086401f2391291514,

title = "Sequential neural models with stochastic layers",

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.",

author = "Marco Fraccaro and S{\o}nderby, {S{\o}ren Kaae} and Ulrich Paquet and Ole Winther",

year = "2016",

language = "English",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural Information Processing Systems Foundation",

pages = "2207--2215",

editor = "Lee, {D. D.} and M. Sugiyama and Luxburg, {U. V.} and I. Guyon and R. Garnett",

booktitle = "Neural Information Processing Systems 2016",

note = "null ; Conference date: 05-12-2016 Through 10-12-2016",

}

RIS

TY - GEN

T1 - Sequential neural models with stochastic layers

AU - Fraccaro, Marco

AU - Sønderby, Søren Kaae

AU - Paquet, Ulrich

AU - Winther, Ole

N1 - Conference code: 30

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85019203505&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:85019203505

T3 - Advances in Neural Information Processing Systems

SP - 2207

EP - 2215

BT - Neural Information Processing Systems 2016

A2 - Lee, D. D.

A2 - Sugiyama, M.

A2 - Luxburg, U. V.

A2 - Guyon, I.

A2 - Garnett, R.

PB - Neural Information Processing Systems Foundation

Y2 - 5 December 2016 through 10 December 2016

ER -

ID: 179362819