Summary of Preventing Model Collapse in Gaussian Process Latent Variable Models, by Ying Li et al.
Preventing Model Collapse in Gaussian Process Latent Variable Models
by Ying Li, Zhidi Lin, Feng Yin, Michael Minyi Zhang
First submitted to arxiv on: 2 Apr 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 advisedRFLVM model addresses common challenges in Gaussian process latent variable modeling by integrating the spectral mixture kernel with a differentiable random Fourier feature approximation. This allows for scalable learning of kernel hyperparameters, projection variance, and latent representations within a variational inference framework. The model is evaluated across diverse datasets, outperforming state-of-the-art VAEs and GPLVM variants in terms of informative latent representations and missing data imputation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new machine learning model called advisedRFLVM to help with dimensionality reduction. They identified two problems that are common when using Gaussian process latent variable models: not having enough flexibility in the model and choosing the wrong level of noise. To solve these issues, they combined two types of kernels (spectral mixture and random Fourier features) and used special tools for automatic differentiation to learn the important parameters. This new model was tested on many different datasets and performed better than other popular models in terms of creating useful hidden representations and filling in missing data. |
Keywords
* Artificial intelligence * Dimensionality reduction * Inference * Machine learning
