论文标题
树结构数据的递归神经网络中的张量分解
Tensor Decompositions in Recursive Neural Networks for Tree-Structured Data
论文作者
论文摘要
本文引入了两个新的聚合功能，以从树结构数据中编码结构知识。他们利用规范和张量训练的分解来产生表达上下文聚集，同时限制模型参数的数量。最后，我们为利用此类聚集功能的树木定义了两个新型的神经递归模型，并在两个树格分类任务上测试了它们，显示了当树超级增加时提出的模型的优势。
The paper introduces two new aggregation functions to encode structural knowledge from tree-structured data. They leverage the Canonical and Tensor-Train decompositions to yield expressive context aggregation while limiting the number of model parameters. Finally, we define two novel neural recursive models for trees leveraging such aggregation functions, and we test them on two tree classification tasks, showing the advantage of proposed models when tree outdegree increases.
