Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
Paper summary Endowing memory to recurrent neural networks is clearly one of the most important topics of deep learning and crucial to do real reasoning. The proposed stack-augmented recurrent nets outperform simple RNN and LSTM \cite{journals/neco/HochreiterS97} on a series of synthetic problems (learning simple algorithmic patterns). The complexity of problems is clearly defined and the behavior of resulting stack RNN could be well understood and easily analyzed. However, the conclusions merely depending on those synthetic data set may take a risk. The importance of the problems to real sequence modeling task could be uncertain and the failures of other models could be greatly improved by more and dense hyper-parameter searching. Like in \cite{journals/corr/LeJH15}, by a very simple trick a RNN works very well on a toy task (a adding problem) which seems to need to model long term dependencies.
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Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
Joulin, Armand and Mikolov, Tomas
Neural Information Processing Systems Conference - 2015 via Bibsonomy
Keywords: dblp


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