论文标题
可解释的动态关系数据建模的经常性迪利奇信念网络
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling
论文作者
论文摘要
Dirichlet信念网络〜（Dirbn）最近被认为是学习对象的可解释的深层表示的有前途的方法。在这项工作中，我们利用其可解释的建模体系结构，并提出了一个深厚的动态概率框架 - 经常性的Dirichlet信念网络〜（Recurrent-DBN） - 从动态关系数据中研究可解释的隐藏结构。拟议的复发型二体具有以下优点：（1）它渗透到时间步骤内外的物体的可解释和有组织的分层潜在结构； （2）它启用了经常性的长期时间依赖模型，在大多数动态概率框架中，它的表现都优于单阶Markov描述。此外，我们开发了一种新的推理策略，该策略首先向上传播潜在的计数，然后向下和前向样品变量，以实现有效的gibbs对复发型DBN进行采样。我们将复发性DBN应用于动态关系数据问题。对现实数据的广泛实验结果验证了在可解释的潜在结构发现和改进的链路预测性能中，复发型二bn的优势比最先进的模型的优势。
The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks. In addition, we develop a new inference strategy, which first upward-and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.
