# Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis

arXiv: https://arxiv.org/abs/2311.03413 · HTML: https://mlsanity.com/paper/2311.03413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [SOM-VAE: Interpretable Discrete Representation Learning on Time Series](https://mlsanity.com/paper/1806.02199/) (2018)
- [Deep Unsupervised Clustering Using Mixture of Autoencoders](https://mlsanity.com/paper/1712.07788/) (2017)

## Abstract (arXiv)

Consistency-based diagnosis is an established approach to diagnose technical applications, but suffers from significant modeling efforts, especially for dynamic multi-modal time series. Machine learning seems to be an obvious solution, which becomes less obvious when looking at details: Which notion of consistency can be used? If logical calculi are still to be used, how can dynamic time series be transferred into the discrete world? This paper presents the methodology Discret2Di for automated learning of logical expressions for consistency-based diagnosis. While these logical calculi have advantages by providing a clear notion of consistency, they have the key problem of relying on a discretization of the dynamic system. The solution presented combines machine learning from both the time series and the symbolic domain to automate the learning of logical rules for consistency-based diagnosis.