Summary of Bayesian Neural Networks with Domain Knowledge Priors, by Dylan Sam et al.
Bayesian Neural Networks with Domain Knowledge Priors
by Dylan Sam, Rattana Pukdee, Daniel P. Jeong, Yewon Byun, J. Zico Kolter
First submitted to arxiv on: 20 Feb 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 This research proposes a framework for integrating domain knowledge into Bayesian neural networks (BNNs) through variational inference, allowing for efficient posterior inference and sampling. The approach assigns high probability mass to models that align with the domain knowledge, leading to improved predictive performance. The authors demonstrate the effectiveness of their method by incorporating diverse types of prior information, such as fairness, physics rules, and healthcare knowledge, outperforming BNNs with standard priors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian neural networks are a type of artificial intelligence that can predict uncertainties in their predictions. This new framework helps make these models better by adding our own expertise and knowledge into the model’s prior beliefs. This makes the model more accurate and reliable. The researchers tested this approach on different types of data, including fairness, physical laws, and healthcare information, and found it worked well. They also showed how to use what was learned with one type of model to improve other models. |
Keywords
* Artificial intelligence * Inference * Probability
