Summary of Double-bayesian Learning, by Stefan Jaeger
Double-Bayesian Learning
by Stefan Jaeger
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 proposed paper presents a novel approach to decision-making, postulating that any decision is composed of two Bayesian decisions. This “double-Bayesian process” implies intrinsic uncertainty in decisions and provides a framework for explainability. The authors show how this duality can be understood by framing Bayesian learning as finding a base for a logarithmic function measuring uncertainty, with solutions being fixed points. Additionally, the paper reveals that the golden ratio describes possible solutions satisfying Bayes’ theorem. To train neural networks using stochastic gradient descent, the double-Bayesian framework suggests employing learning rates and momentum weights similar to those used in the literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how we make decisions. It says that every decision is actually two smaller decisions made together. This means that when we decide something, there’s always some uncertainty involved. The authors also show how this way of thinking can help us understand why certain things happen or don’t happen. They use special math ideas like logarithms and the golden ratio to explain their points. |
Keywords
» Artificial intelligence » Stochastic gradient descent
