Distributed Associative Memory Network with Memory Refreshing Loss

TL;DR

This paper introduces a novel distributed associative memory network with a memory refreshing loss that improves relational reasoning in memory-augmented neural networks by mimicking brain-like memory rehearsal and updating multiple memory blocks simultaneously.

Contribution

The paper proposes a Distributed Associative Memory architecture with Memory Refreshing Loss, enhancing relational reasoning by encoding data across multiple memory blocks and reinforcing associations during learning.

Findings

Achieved state-of-the-art performance on memorization tasks.

Improved relational reasoning capabilities in memory-augmented neural networks.

Demonstrated effectiveness with Differential Neural Computer (DNC).

Abstract

Despite recent progress in memory augmented neural network (MANN) research, associative memory networks with a single external memory still show limited performance on complex relational reasoning tasks. Especially the content-based addressable memory networks often fail to encode input data into rich enough representation for relational reasoning and this limits the relation modeling performance of MANN for long temporal sequence data. To address these problems, here we introduce a novel Distributed Associative Memory architecture (DAM) with Memory Refreshing Loss (MRL) which enhances the relation reasoning performance of MANN. Inspired by how the human brain works, our framework encodes data with distributed representation across multiple memory blocks and repeatedly refreshes the contents for enhanced memorization similar to the rehearsal process of the brain. For this procedure, we replace a single external memory with a set of multiple smaller associative memory blocks and update these sub-memory blocks simultaneously and independently for the distributed representation of input data. Moreover, we propose MRL which assists a task's target objective while learning relational information existing in data. MRL enables MANN to reinforce an association between input data and task objective by reproducing stochastically sampled input data from stored memory contents. With this procedure, MANN further enriches the stored representations with relational information. In experiments, we apply our approaches to Differential Neural Computer (DNC), which is one of the representative content-based addressing memory models and achieves the state-of-the-art performance on both memorization and relational reasoning tasks.