Summary of Latent Variable Model For High-dimensional Point Process with Structured Missingness, by Maksim Sinelnikov et al.
Latent variable model for high-dimensional point process with structured missingness
by Maksim Sinelnikov, Manuel Haussmann, Harri Lähdesmäki
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a new latent-variable model that can handle high-dimensional longitudinal data with structured missingness patterns and unknown temporal correlations. The model uses Gaussian processes to capture these complexities, and is designed as a variational autoencoder with an amortised inference approach for efficient training. The approach demonstrates competitive performance on both simulated and real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed a new machine learning model that can handle challenging types of data. Longitudinal data often have missing values, complex patterns, and unknown timing. The proposed model uses special techniques to understand these complexities and predict the missing values accurately. The results show that the model performs well on both made-up and real-world datasets. |
Keywords
* Artificial intelligence * Inference * Machine learning * Variational autoencoder
