A theoretically grounded application of dropout in recurrent neural networks
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dropout shown to fail when applied to recurrent layers. Recent results at the intersection of Bayesian modelling and deep...
| Hauptverfasser: | , |
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| Format: | Conference item |
| Sprache: | English |
| Veröffentlicht: |
Massachusetts Institute of Technology Press
2016
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