Summary of Nonparametric Automatic Differentiation Variational Inference with Spline Approximation, by Yuda Shao et al.
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation
by Yuda Shao, Shan Yu, Tianshu Feng
First submitted to arxiv on: 10 Mar 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 paper proposes a new approach to variational inference, building on Automatic Differentiation Variational Inference (ADVI). The classic parametric ADVI has limitations when dealing with complicated distributions, such as skewness, multimodality, and bounded support. To address this, the authors develop a spline-based nonparametric approximation method that enables flexible posterior approximation. This approach is shown to be efficient in approximating complex posterior distributions and improving the performance of generative models with incomplete data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to use ADVI to learn probabilistic models. It’s hard for classic ADVI to work well when the distribution has weird shapes, like being skewed or having multiple peaks. The authors create a new method that uses splines to approximate the posterior distribution, which is more flexible and easy to use than other nonparametric methods. They also show how this method can be used with importance weighted autoencoders to improve generative models. |
Keywords
* Artificial intelligence * Inference
