论文标题
多电脑张量网络
Multi-Graph Tensor Networks
论文作者
论文摘要
众多现代数据源的不规则和多模式性质为传统的深度学习算法带来了严重的挑战。为此，最近的努力将现有算法概括为通过图形的不规则域，目的是通过基础图形拓扑从数据中获得更多见解。同时，基于张量的方法在绕过尺寸诅咒所施加的瓶颈方面表现出了有希望的结果。在本文中，我们介绍了一种新型的多毛牌张量网络（MGTN）框架，该框架利用了图形处理不规则数据源的能力，又利用了在深度学习环境中张量网络的压缩属性。提出的框架的潜力通过基于MGTN的Deep Q代理（外汇）算法交易证明。借助MGTN，借用了外汇货币图在这项苛刻的任务上施加了经济上有意义的结构，从而在三个竞争模型上产生了非常出色的性能，并且较低的复杂性。
The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph topology. At the same time, tensor-based methods have demonstrated promising results in bypassing the bottlenecks imposed by the Curse of Dimensionality. In this paper, we introduce a novel Multi-Graph Tensor Network (MGTN) framework, which exploits both the ability of graphs to handle irregular data sources and the compression properties of tensor networks in a deep learning setting. The potential of the proposed framework is demonstrated through an MGTN based deep Q agent for Foreign Exchange (FOREX) algorithmic trading. By virtue of the MGTN, a FOREX currency graph is leveraged to impose an economically meaningful structure on this demanding task, resulting in a highly superior performance against three competing models and at a drastically lower complexity.
