Abstract:
The human brain employs sophisticated schemes to store and reuse accumulated
information. Episodic memory is one such strategy: temporally
dependent event sequences are encoded and stored, and then replayed for
beneficial effects for the collected information. In this research an algorithm is
employed where episodes are created by a series of stacked Recurrent Neural
Networks (RNNs). Information at different points of the processing is stored
as the hidden states of the corresponding RNN. Another RNN is subsequently
used to generate episodes given partial information of the encoded episodes,
and a pre-set intended answer. The results show that the use of novel episodic
replay allows the network to increase the speed of convergence in some of the
20 QA bAbI tasks.
