Wang, Yuanrong, Briola, Antonio and Aste, Tomaso (2023) Homological neural networks: a sparse architecture for multivariate complexity. Proceedings of Machine Learning Research, 221. 228 - 241. ISSN 1938-7228
Full text not available from this repository.Abstract
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. The code and the data are available at https://github.com/FinancialComputingUCL/HNN.
| Item Type: | Article |
|---|---|
| Official URL: | https://proceedings.mlr.press/v221/ |
| Additional Information: | © 2023 The Author(s) |
| Divisions: | Systemic Risk Centre |
| Subjects: | Q Science > Q Science (General) |
| Date Deposited: | 15 Dec 2023 10:33 |
| Last Modified: | 18 Nov 2024 20:24 |
| URI: | http://eprints.lse.ac.uk/id/eprint/121062 |
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