论文标题
发现非线性关系与最低预测信息正则化
Discovering Nonlinear Relations with Minimum Predictive Information Regularization
论文作者
论文摘要
通过非线性相互作用和复杂的关系结构从观察时间序列中识别潜在的定向关系是广泛应用的关键，但仍然是一个严重的问题。在这项工作中，我们介绍了一种新颖的最低预测信息正则化方法，以从时间序列推断方向关系，从而使深度学习模型发现非线性关系。我们的方法基本上优于在合成数据集中学习非线性关系的其他方法，并发现视频游戏环境中的定向关系以及心率与呼吸速率数据集。
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.
