Summary of Structured Partial Stochasticity in Bayesian Neural Networks, by Tommy Rochussen
Structured Partial Stochasticity in Bayesian Neural Networks
by Tommy Rochussen
First submitted to arxiv on: 27 May 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 Bayesian neural network posterior distributions have a multitude of modes corresponding to identical network functions. This abundance hinders the effectiveness of approximate inference methods. Recent research showcases the benefits of partial stochasticity in Bayesian neural networks; approximate inference can be more cost-effective and performance may improve. I propose a structured approach to select deterministic weights, eliminating neuron permutation symmetries and redundant posterior modes. With a significantly simplified posterior distribution, existing approximate inference schemes exhibit improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special kind of computer program called a neural network that helps make predictions or decisions. Sometimes these programs can get stuck because there are too many possible solutions. Scientists have found that making some parts of the program random and others fixed can help it work better and faster. Now, researchers propose an organized way to choose which parts should be fixed, allowing the program to focus on one solution instead of many. This makes existing methods for finding the best predictions or decisions much more effective. |
Keywords
» Artificial intelligence » Inference » Neural network
