Fast Weight Long Short-Term Memory

T. Anderson Keller, Sharath Nittur Sridhar, Xin Wang

Feb 12, 2018 (modified: Jun 04, 2018) ICLR 2018 Workshop Submission readers: everyone Show Bibtex
  • Abstract: Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs). As recent studies introduced fast weights only to regular RNNs, it is unknown whether fast weight memory is beneficial to gated RNNs. In this work, we report a significant synergy between long short-term memory (LSTM) networks and fast weight associative memories. We show that this combination, in learning associative retrieval tasks, results in much faster training and lower test error, a performance boost most prominent at high memory task difficulties.
  • Keywords: Fast Weights, LSTM, Long Short-Term Memory, Associative Memory, Recurrent, RNN
  • TL;DR: We show that LSTM with fast weight associative memory trains much faster and achieves lower test error in associative retrieval tasks than previously reported fast weights RNNs.

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