Summary of Function-space Mcmc For Bayesian Wide Neural Networks, by Lucia Pezzetti et al.
Function-Space MCMC for Bayesian Wide Neural Networks
by Lucia Pezzetti, Stefano Favaro, Stefano Peluchetti
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The abstract discusses the application of Bayesian Neural Networks to uncertainty modeling in complex predictive models. Researchers investigate the use of preconditioned Crank-Nicolson and Langevin algorithms for sampling from the reparametrised posterior distribution of neural network weights, focusing on robustness and efficiency as the network width increases. The study finds that the preconditioned Crank-Nicolson algorithm is particularly effective in wide Bayesian Neural Networks configurations, allowing for efficient sampling of the posterior distribution with improved diagnostic results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to use special algorithms called Bayesian Neural Networks to understand uncertainty in complex models. Scientists developed new ways to use these algorithms, called preconditioned Crank-Nicolson and Langevin, to sample from the model’s weights as it gets bigger. They found that one of these methods works really well when the network is wide, making it easier to get accurate results. |
Keywords
* Artificial intelligence * Neural network
