Summary of Latent Mixed-effect Models For High-dimensional Longitudinal Data, by Priscilla Ong et al.
Latent mixed-effect models for high-dimensional longitudinal data
by Priscilla Ong, Manuel Haußmann, Otto Lönnroth, Harri Lähdesmäki
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); Machine Learning (stat.ML)
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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 modelling longitudinal data is presented, leveraging linear mixed models (LMMs) and amortized variational inference to provide conditional priors for Gaussian process (GP) prior-based variational autoencoders (VAEs). The proposed LMM-VAE model offers scalability, interpretability, and identifiability, addressing limitations of existing GP-based VAE methods. This unified framework provides a connection between GP-based techniques and LMM-VAEs, enabling practitioners to effectively utilize longitudinal data. The authors evaluate their proposal on simulated and real-world datasets, demonstrating competitive performance compared to existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed a new way to analyze data that changes over time. This type of data is common in many fields, such as medicine and social sciences. The old method was not very efficient or easy to use. In this study, the researchers created a new approach called LMM-VAE. It’s based on two other methods: linear mixed models and variational autoencoders. This new method is faster, easier to understand, and can be used with many types of data. The authors tested their idea using fake and real datasets, and it worked well compared to other approaches. |
Keywords
» Artificial intelligence » Inference
