ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network

Understand

In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks.

• However, previous MANNs suffer from complex memory addressing mechanism, making them relatively hard to train and causing computational overheads.
• Moreover, many of them reuse the classical RNN structure such as LSTM for memory processing, causing inefficient exploitations of memory information.
• In this paper, we introduce a novel MANN, the Auto-addressing and Recurrent Memory Integrating Network (ARMIN) to address these issues.