论文标题
自动简化机器：利用分段线性神经网络的结构来创建可解释的模型
The Self-Simplifying Machine: Exploiting the Structure of Piecewise Linear Neural Networks to Create Interpretable Models
论文作者
论文摘要
如今，用户对使用他们使用的模型有信任比以往任何时候都重要。随着机器学习模型受到增加的监管审查，并开始在高风险情况下看到更多的应用，解释我们的模型变得至关重要。由于许多吸引人的属性，具有RELU激活功能的分段线性神经网络（PLNN）已迅速成为极为流行的模型。但是，他们仍然在鲁棒性和解释领域提出许多挑战。为此，我们介绍了新颖的方法，以简化和提高分类线性神经网络的解释性来分类任务。我们的方法包括使用经过训练的深层网络来生成一个表现出色的单隐范网络，而无需进一步的随机训练，除了将算法减少到较小，更容易解释的尺寸，性能损失最小。在这些方法上，我们对富国银行的家庭贷款数据集进行了模型性能的初步研究，以及视觉模型解释。
Today, it is more important than ever before for users to have trust in the models they use. As Machine Learning models fall under increased regulatory scrutiny and begin to see more applications in high-stakes situations, it becomes critical to explain our models. Piecewise Linear Neural Networks (PLNN) with the ReLU activation function have quickly become extremely popular models due to many appealing properties; however, they still present many challenges in the areas of robustness and interpretation. To this end, we introduce novel methodology toward simplification and increased interpretability of Piecewise Linear Neural Networks for classification tasks. Our methods include the use of a trained, deep network to produce a well-performing, single-hidden-layer network without further stochastic training, in addition to an algorithm to reduce flat networks to a smaller, more interpretable size with minimal loss in performance. On these methods, we conduct preliminary studies of model performance, as well as a case study on Wells Fargo's Home Lending dataset, together with visual model interpretation.
