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arxiv: 1412.6581 · v6 · pith:KQTZ5FCQnew · submitted 2014-12-20 · 📊 stat.ML · cs.LG· cs.NE

Variational Recurrent Auto-Encoders

Otto Fabius , Joost R. van Amersfoort This is my paper
classification 📊 stat.ML cs.LGcs.NE
keywords datamodellatentrecurrentrnnsseriestimevariational
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Approximately Equivariant Recurrent Generative Models for Quasi-Periodic Time Series with a Progressive Training Scheme

    cs.LG 2025-05 unverdicted novelty 5.0

    AEQ-RVAE-ST combines approximate equivariance and progressive sequence lengthening in a recurrent VAE to match or exceed prior generative models on quasi-periodic time series benchmarks.