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.

翻译：人工智能研究的飞速发展伴随着日益复杂的深度学习模型的出现，从而在计算复杂性、能效和可解释性方面带来日益严峻的挑战。在本研究中，我们应用先进的基于网络的信息过滤技术，设计了一种新型深度神经网络单元，其特点是基于底层数据的同调结构构建的稀疏高阶图架构。我们在两个传统上对深度学习具有挑战性的应用领域（表格数据和时间序列回归问题）中证明了其有效性。结果表明，这种新颖设计具有显著优势，仅使用极少量的参数即可达到或超越当前最先进的机器学习和深度学习模型的结果。