论文标题
神经添加剂模型
Neural Additive Models for Nowcasting
论文作者
论文摘要
深神经网络（DNN）是机器学习中最突出的方法之一。但是，由于DNN是黑盒模型，因此它们缺乏预测的解释能力。最近，已经提出了神经添加剂模型（NAM）来提供此功能，同时保持高预测性能。在本文中，我们提出了一种新颖的NAM方法，用于多元启发（NC）问题，其中构成了机器学习的重要重点领域。对于NC问题中使用的多元时间序列数据，应在可区分的时间步骤中为变量的每个输入值考虑说明。通过采用广义的加性模型，提出的NAM-NC成功地解释了每个输入值对多个变量和时间步骤的重要性。涉及玩具示例和两个现实数据集的实验结果表明，NAM-NC预测多元时间序列数据与最新神经网络一样准确，同时还提供了每个输入值的解释重要性。我们还使用NAM-NC检查了参数共享网络以降低其复杂性，并且NAM-MC的固定功能净提取的解释具有良好的性能。
Deep neural networks (DNNs) are one of the most highlighted methods in machine learning. However, as DNNs are black-box models, they lack explanatory power for their predictions. Recently, neural additive models (NAMs) have been proposed to provide this power while maintaining high prediction performance. In this paper, we propose a novel NAM approach for multivariate nowcasting (NC) problems, which comprise an important focus area of machine learning. For the multivariate time-series data used in NC problems, explanations should be considered for every input value to the variables at distinguishable time steps. By employing generalized additive models, the proposed NAM-NC successfully explains each input value's importance for multiple variables and time steps. Experimental results involving a toy example and two real-world datasets show that the NAM-NC predicts multivariate time-series data as accurately as state-of-the-art neural networks, while also providing the explanatory importance of each input value. We also examine parameter-sharing networks using NAM-NC to decrease their complexity, and NAM-MC's hard-tied feature net extracted explanations with good performance.
