Summary of Variational Learning Of Gaussian Process Latent Variable Models Through Stochastic Gradient Annealed Importance Sampling, by Jian Xu et al.
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
by Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Bayesian Gaussian Process Latent Variable Models (GPLVMs) is proposed for unsupervised tasks like dimensionality reduction and missing data recovery. The importance-weighted GPLVM provides a tighter variational bound, but its application is limited by the complexity of proposal distributions in high-dimensional spaces or complex datasets. To overcome this limitation, an Annealed Importance Sampling (AIS) method is introduced, combining Sequential Monte Carlo samplers and Variational Inference (VI). The AIS approach transforms the posterior into intermediate distributions using annealing, allowing for a wider range of posterior exploration and gradual convergence to the target distribution. Additionally, an efficient algorithm reparameterizes all variables in the evidence lower bound (ELBO) to improve performance. Experimental results demonstrate the proposed method outperforms state-of-the-art methods on toy and image datasets in terms of tighter variational bounds, higher log-likelihoods, and more robust convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian Process Latent Variable Models are a type of machine learning that helps find patterns in data without labels. They’re good at reducing the number of features needed to describe data and filling in missing values. However, they can be tricky to use with big datasets or complex data. To make them more useful, researchers developed an Annealed Importance Sampling method that helps the model explore a wider range of possible solutions and find the best one. This approach is faster and more accurate than previous methods. It works well on simple and image datasets. |
Keywords
» Artificial intelligence » Dimensionality reduction » Inference » Machine learning » Unsupervised
