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Papers

A Recurrent Latent Variable Model for Sequential Data

2015-06-07 · NeurIPS 2015 12 · Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio

In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamic hidden state.

📄 PDF Abstract BibTeX arXiv:1506.02216

Code (5)

jych/nips2015_vrnn 공식 구현
dgedon/DeepSSM_SysID pytorch
emited/VariationalRecurrentNeuralNetwork pytorch
joe5saia/PanelVariationalReccurentAutoEncoder
mazrk7/discvae tf

Tasks

model

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