Modifying memories in a Recurrent Neural Network Unit

Vlad Velici, Adam Prügel-Bennett

Feb 15, 2018 (modified: Oct 27, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data. We introduce the concept of modifying the cell state (memory) of LSTMs using rotation matrices parametrised by a new set of trainable weights. This addition shows significant increases of performance on some of the tasks from the bAbI dataset.
  • TL;DR: Adding a new set of weights to the LSTM that rotate the cell memory improves performance on some bAbI tasks.
  • Keywords: LSTM, RNN, rotation matrix, long-term memory, natural language processing
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