Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization

Understand

Learning to remember long sequences remains a challenging task for recurrent neural networks.

• Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shorter local contexts than encouraging long sequence encoding.
• Associative memory, which studies the compression of multiple patterns in a fixed size memory, were rarely considered in recent years.
• Although some recent work tries to introduce associative memory in RNN and mimic the energy decay process in Hopfield nets, it inherits the shortcoming of rule-based memory updates, and the memory capacity is limited.