Discovering Nonlinear Relations with Minimum Predictive Information Regularization

Autor:	Wu, Tailin, Breuel, Thomas, Skuhersky, Michael, Kautz, Jan
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Identifying the underlying directional relations from observational time series with nonlinear interactions and complex relational structures is key to a wide range of applications, yet remains a hard problem. In this work, we introduce a novel minimum predictive information regularization method to infer directional relations from time series, allowing deep learning models to discover nonlinear relations. Our method substantially outperforms other methods for learning nonlinear relations in synthetic datasets, and discovers the directional relations in a video game environment and a heart-rate vs. breath-rate dataset. Comment: 26 pages, 11 figures; ICML'19 Time Series Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.01885 Zobrazit plný text záznamu View this record from Arxiv