Authors
Wojciech Zaremba, Ilya Sutskever
Publication date
2014/10/17
Journal
arXiv preprint arXiv:1410.4615
Description
Abstract: Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM)
are widely used because they are expressive and are easy to train. Our interest lies in
empirically evaluating the expressiveness and the learnability of LSTMs in the sequence-to-
sequence regime by training them to evaluate short computer programs, a domain that has
traditionally been seen as too complex for neural networks. We consider a simple class of
programs that can be evaluated with a single left-to-right pass using constant memory. Our ...
are widely used because they are expressive and are easy to train. Our interest lies in
empirically evaluating the expressiveness and the learnability of LSTMs in the sequence-to-
sequence regime by training them to evaluate short computer programs, a domain that has
traditionally been seen as too complex for neural networks. We consider a simple class of
programs that can be evaluated with a single left-to-right pass using constant memory. Our ...
Total citations
Scholar articles
W Zaremba, I Sutskever - arXiv preprint arXiv:1410.4615, 2014
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