论文标题
关于基于决策树的神经图灵机的简短说明
A short note on the decision tree based neural turing machine
论文作者
论文摘要
图灵机和决策树已经独立开发了很长时间。随着可区分模型的最新发展，它们之间存在一个相交。神经图灵机（NTM）为存储网络打开门。它使用可不同的注意机制来读/写外部记忆库。可区分的森林为经典决策树带来了可区分的特性。在此简短说明中，我们显示了这两个模型之间的深厚联系。也就是说：可区分的森林是NTM的特殊情况。可区分的森林实际上是基于决策树的神经图灵机。基于这种深厚的联系，我们提出了一个响应增强的差异森林（RADF）。 RADF的控制器是可区分的森林，RADF的外部内存是响应向量，将由叶子节点读/写。
Turing machine and decision tree have developed independently for a long time. With the recent development of differentiable models, there is an intersection between them. Neural turing machine(NTM) opens door for the memory network. It use differentiable attention mechanism to read/write external memory bank. Differentiable forest brings differentiable properties to classical decision tree. In this short note, we show the deep connection between these two models. That is: differentiable forest is a special case of NTM. Differentiable forest is actually decision tree based neural turing machine. Based on this deep connection, we propose a response augmented differential forest (RaDF). The controller of RaDF is differentiable forest, the external memory of RaDF are response vectors which would be read/write by leaf nodes.
