Summary of A Deep Latent Variable Model For Semi-supervised Multi-unit Soft Sensing in Industrial Processes, by Bjarne Grimstad et al.
A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes
by Bjarne Grimstad, Kristian Løvland, Lars S. Imsland, Vidar Gunnerud
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to developing data-driven soft sensors in industrial processes leverages knowledge about the data to learn more efficient models. The proposed deep latent variable model can semi-supervisedly learn to sense multiple units using both labeled and unlabeled data, effectively modeling relationships between different units. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Industrial processes often have limited data availability, making it challenging to develop data-driven soft sensors. However, by being more data-efficient, stronger models can be learned. A new deep latent variable model is introduced for semi-supervised multi-unit soft sensing, allowing joint modeling of multiple units and learning from both labeled and unlabeled data. |
Keywords
* Artificial intelligence * Semi supervised
