Malinovskaya, A., Mozharovskyi, P. and Otto, P. (2024) Statistical process monitoring of artificial neural networks. Technometrics, 66(1), pp. 104-117. (doi: 10.1080/00401706.2023.2239886)
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Abstract
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.
| Item Type: | Articles |
|---|---|
| Additional Information: | The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 412992257. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Otto, Dr Philipp |
| Authors: | Malinovskaya, A., Mozharovskyi, P., and Otto, P. |
| College/School: | College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Technometrics |
| Publisher: | Taylor & Francis |
| ISSN: | 0040-1706 |
| ISSN (Online): | 1537-2723 |
| Published Online: | 25 July 2023 |
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