# Statistical process monitoring of artificial neural networks

arXiv: https://arxiv.org/abs/2209.07436 · HTML: https://mlsanity.com/paper/2209.07436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 143 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 24 of 24)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (2018)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [Confidence-Aware Learning for Deep Neural Networks](https://mlsanity.com/paper/2007.01458/) (2020)
- [Generative Probabilistic Novelty Detection with Adversarial Autoencoders](https://mlsanity.com/paper/1807.02588/) (2018)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)
- [Understanding Softmax Confidence and Uncertainty](https://mlsanity.com/paper/2106.04972/) (2021)
- [On the Dimensionality of Word Embedding](https://mlsanity.com/paper/1812.04224/) (2018)
- [A Framework to Learn with Interpretation](https://mlsanity.com/paper/2010.09345/) (2020)
- [Choosing among notions of multivariate depth statistics](https://mlsanity.com/paper/2004.01927/) (2020)
- [Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors](https://mlsanity.com/paper/2012.06916/) (2020)
- [Data Twinning](https://mlsanity.com/paper/2110.02927/) (2021)
- [Sparse Sliced Inverse Regression Via Lasso](https://mlsanity.com/paper/1611.06655/) (2016)
- [Depth and depth-based classification with R-package ddalpha](https://mlsanity.com/paper/1608.04109/) (2016)
- [Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders](https://mlsanity.com/paper/1911.00482/) (2019)
- [Distributionally robust halfspace depth](https://mlsanity.com/paper/2101.00726/) (2021)

## Built on by (most cited first, 1 of 1)

- [Bootstrapped Control Limits for Score-Based Concept Drift Control Charts](https://mlsanity.com/paper/2507.16749/) (2025)

## Abstract (arXiv)

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.