论文标题
融合复发神经网络
Fusion Recurrent Neural Network
论文作者
论文摘要
考虑到实用应用深度序列学习，两个代表性的RNN -LSTM和GRU可能首先想到。但是，其他RNN是否没有机会？将来会有更好的RNN吗？在这项工作中，我们提出了一种新颖，简洁而有前途的RNN融合复发性神经网络（Fusion RNN）。 Fusion RNN由融合模块和传输模块组成。 Fusion模块实现了输入和隐藏状态向量的多轮融合。运输模块主要是指简单的重复网络计算隐藏状态，并准备将其传递到下一个时间步骤。此外，为了评估Fusion RNN的序列特征提取能力，我们为序列数据，估计到达时间（ETA）选择了代表性数据挖掘任务，并提供了基于Fusion RNN的新型模型。在DIDI CHUXING的大量车辆旅行数据下，我们将我们的方法和其他RNN的方法与RNN的其他变体进行了对比。结果表明，对于ETA，融合RNN与最先进的LSTM和GRU相当，它们比融合RNN更为复杂。
Considering deep sequence learning for practical application, two representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs? Will there be a better RNN in the future? In this work, we propose a novel, succinct and promising RNN - Fusion Recurrent Neural Network (Fusion RNN). Fusion RNN is composed of Fusion module and Transport module every time step. Fusion module realizes the multi-round fusion of the input and hidden state vector. Transport module which mainly refers to simple recurrent network calculate the hidden state and prepare to pass it to the next time step. Furthermore, in order to evaluate Fusion RNN's sequence feature extraction capability, we choose a representative data mining task for sequence data, estimated time of arrival (ETA) and present a novel model based on Fusion RNN. We contrast our method and other variants of RNN for ETA under massive vehicle travel data from DiDi Chuxing. The results demonstrate that for ETA, Fusion RNN is comparable to state-of-the-art LSTM and GRU which are more complicated than Fusion RNN.
