Summary of Correlating Variational Autoencoders Natively For Multi-view Imputation, by Ella S. C. Orme et al.
Correlating Variational Autoencoders Natively For Multi-View Imputation
by Ella S. C. Orme, Marina Evangelou, Ulrich Paquet
First submitted to arxiv on: 5 Nov 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 The proposed multi-view VAE approach incorporates a joint prior with a non-zero correlation structure between the latent spaces of separate VAEs trained on each data-view. By enforcing this correlation structure, more strongly correlated latent spaces are uncovered, allowing for imputation and downstream analysis. The method learns the correlation structure while maintaining validity of the prior distribution and successfully parameterizing end-to-end learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to analyze multiple types of data that come from the same source but have different views. They found that these views are connected in a special way, which they call “correlation”. To take advantage of this connection, they created a new type of machine learning model called a multi-view VAE. This model can find patterns in the data and even fill in missing information by using the connections between the different views. |
Keywords
* Artificial intelligence * Machine learning
