Statistical Process Monitoring of Artificial Neural Networks

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called “embedding”) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.

基于人工智能的模型快速发展，亟需能够实时运行且计算成本低廉的创新监测技术。在机器学习领域，尤其是针对人工神经网络（Artificial Neural Networks，ANNs），模型通常采用监督学习方式进行训练。因此，在模型部署阶段，其学习得到的输入与输出之间的关联关系必须保持稳定有效。若该平稳性假设成立，则可认定该人工神经网络能够输出准确的预测结果；反之，则需对模型进行重新训练或重建。本文提出借助人工神经网络生成的数据潜在特征表示（称为"嵌入（embedding）"），来判定数据流何时开始出现非平稳性。具体而言，我们通过基于数据深度计算与归一化秩的多元控制图来监测嵌入结果。针对多种人工神经网络架构与不同底层数据格式，本文将所提方法的性能与各类基准方法进行了对比。