Summary of Implicit Generative Prior For Bayesian Neural Networks, by Yijia Liu et al.
Implicit Generative Prior for Bayesian Neural Networks
by Yijia Liu, Xiao Wang
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP)
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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 This paper proposes a novel neural adaptive empirical Bayes (NA-EB) framework for predictive uncertainty quantification. Bayesian neural networks are a powerful tool for this task, but defining meaningful priors and ensuring computational efficiency remain significant challenges. The NA-EB framework addresses these issues by leveraging low-dimensional distributions to derive implicit generative priors, allowing for efficient handling of complex data structures and effective capture of underlying relationships in real-world datasets. The framework combines variational inference with a gradient ascent algorithm, enabling simultaneous hyperparameter selection and approximation of the posterior distribution, leading to improved computational efficiency. Experimental evaluations demonstrate the superiority of this framework over existing methods on various tasks, including regression, image classification, and uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making predictions more reliable by understanding how uncertain they are. It uses special kinds of neural networks called Bayesian neural networks, which can help with this task. But there are some problems to overcome, like finding the right starting point for these networks and making sure they don’t take too long to work through the data. The new framework solves these issues by using simpler ideas to create more complex models, allowing it to handle big datasets and capture hidden patterns. It does this by combining two different techniques: one that helps find the best settings for the model, and another that lets us understand what the model is doing as it makes predictions. The results show that this new framework works better than others on a range of tasks. |
Keywords
» Artificial intelligence » Hyperparameter » Image classification » Inference » Regression
