The rapid progress of Artificial Intelligence research came with the
development of increasingly complex deep learning models, leading to growing
challenges in terms of computational complexity, energy efficiency and
interpretability. In this study, we apply advanced network-based information
filtering techniques to design a novel deep neural network unit characterized
by a sparse higher-order graphical architecture built over the homological
structure of underlying data. We demonstrate its effectiveness in two
application domains which are traditionally challenging for deep learning:
tabular data and time series regression problems. Results demonstrate the
advantages of this novel design which can tie or overcome the results of
state-of-the-art machine learning and deep learning models using only a
fraction of parameters.

本研究应用基于网络的信息过滤技术设计了一种新的深度神经网络单元，其具有由基础数据的同调结构构建的稀疏高阶图形架构，同时证明它在传统难以解决的领域，如表格数据和时间序列回归问题中的有效性，仅使用一小部分参数即可超越最先进的机器学习和深度学习模型的结果。