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arXiv:1411.7610stat.ML

Learning Stochastic Recurrent Networks

Justin Bayer, Christian Osendorfer

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Abstract

Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trained with stochastic gradient methods, ii) allows structured and multi-modal conditionals at each time step, iii) features a reliable estimator of the marginal likelihood and iv) is a generalisation of deterministic recurrent neural networks. We evaluate the method on four polyphonic musical data sets and motion capture data.

arXiv comments
Submitted to conference track of ICLR 2015
Journal reference
DOI
Subjects
stat.MLMachine Learning (Statistics) cs.LGMachine Learning

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  1. Classical music generation with recurrent neural networks 1 comment

    • This is really cool stuff - the network structure reminds me a lot of Graves' MDRNN[1] and Grid LSTM[2], as well as some work I helped with (ReNet [3]) I wonder if the structure over frequency/time is too "regular" - in general for sound the frequency correlation and the time…

      kastnerkyleView on HNParent context

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