Variational Recurrent Auto-Encoders

Abstract

In this paper we propose a model that combines the strengths of RNNs and SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used for efficient, large scale unsupervised learning on time series data, mapping the time series data to a latent vector representation. The model is generative, such that data can be generated from samples of the latent space. An important contribution of this work is that the model can make use of unlabeled data in order to facilitate supervised training of RNNs by initialising the weights and network state.

Accepted at ICLR workshop track

References in corpus (4)

• Sequence to Sequence Learning with Neural Networks
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
• Learning Stochastic Recurrent Networks

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• Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
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• A Benchmark of Dynamical Variational Autoencoders applied to Speech Spectrogram Modeling
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder