Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks

Previous algorithms for supervised sequence learning are based on dynamic recurrent networks. This paper describes an alternative class of gradient-based systems consisting of two
feedforward
nets that learn to deal with temporal sequences using
fast weights:
The first net
learns
to produce context-dependent weight
changes
for the second net whose weights may vary very quickly. The method offers the potential for STM storage efficiency: A single weight (instead of a full-fledged unit) may be sufficient for storing temporal information. Various learning methods are derived. Two experiments with unknown time delays illustrate the approach. One experiment shows how the system can be used for
adaptive
temporary variable binding.