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Relational dynamic memory networks

2018-08-10 00:01:34
Trang Pham, Truyen Tran, Svetha Venkatesh

Abstract

Working memory is an essential component of reasoning -- the capacity to answer a new question by manipulating acquired knowledge. Current memory-augmented neural networks offer a differentiable method to realize limited reasoning with support of a working memory module. Memory modules are often implemented as a set of memory slots without explicit relational exchange of content. This does not naturally match multi-relational domains in which data is structured. We design a new model dubbed Relational Dynamic Memory Network (RDMN) to fill this gap. The memory can have a single or multiple components, each of which realizes a multi-relational graph of memory slots. The memory is dynamically updated in the reasoning process controlled by the central controller. We evaluate the capability of RDMN on several important application domains: software vulnerability, molecular bioactivity and chemical reaction. Results demonstrate the efficacy of the proposed model.

Abstract (translated)

工作记忆是推理的重要组成部分 - 通过操纵获得的知识来回答新问题的能力。当前的存储器增强神经网络提供了一种可区分的方法,以通过工作存储器模块的支持来实现有限的推理。存储器模块通常被实现为一组存储器槽而没有明确的内容关系交换。这自然不匹配数据结构化的多关系域。我们设计了一个名为关系动态内存网络(RDMN)的新模型来填补这一空白。存储器可以具有单个或多个组件,每个组件实现存储器槽的多关系图。在由中央控制器控制的推理过程中动态更新存储器。我们评估了RDMN在几个重要应用领域的能力:软件脆弱性,分子生物活性和化学反应。结果证明了所提出的模型的功效。

URL

https://arxiv.org/abs/1808.04247

PDF

https://arxiv.org/pdf/1808.04247.pdf


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