Summary of Integrating Random Effects in Variational Autoencoders For Dimensionality Reduction Of Correlated Data, by Giora Simchoni et al.
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data
by Giora Simchoni, Saharon Rosset
First submitted to arxiv on: 22 Dec 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 model called LMMVAE is proposed to improve Variational Autoencoders (VAEs) for dimensionality reduction of large-scale datasets with correlated data observations. This is achieved by separating the VAE latent model into fixed and random parts, with the fixed part assuming independent latent variables as usual and the random part introducing correlation between similar clusters in the data. The modified LMMVAE architecture and loss are shown to significantly improve squared reconstruction error and negative likelihood loss on unseen data across various applications and correlation scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LMMVAE is a new way to make VAEs work better when our data has patterns or connections between different parts of it. Instead of assuming that everything is independent, like we usually do, LMMVAE splits the model into two parts: one that assumes independence, and another that lets similar things be connected. This helps VAEs learn a more accurate representation of our data, which can lead to better results in tasks like classification. |
Keywords
» Artificial intelligence » Classification » Dimensionality reduction » Likelihood
