Summary of Towards Model-agnostic Posterior Approximation For Fast and Accurate Variational Autoencoders, by Yaniv Yacoby et al.
Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders
by Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 authors propose an alternative approach to inference for Variational Autoencoders (VAEs), which learns the generative and inference models independently. The method approximates the posterior of the true model using a deterministic, model-agnostic posterior approximation (MAPA). This allows for efficient optimization and outperforms baselines in density estimation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to train Variational Autoencoders (VAEs) by learning the generative and inference models separately. The authors suggest that this method can improve performance and reduce computation costs compared to traditional methods. They also show preliminary results on low-dimensional data that support the effectiveness of their approach. |
Keywords
* Artificial intelligence * Density estimation * Inference * Optimization
