Summary of On the Hardness Of Probabilistic Neurosymbolic Learning, by Jaron Maene et al.
On the Hardness of Probabilistic Neurosymbolic Learning
by Jaron Maene, Vincent Derkinderen, Luc De Raedt
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes an approach to improve probabilistic neurosymbolic models by studying the complexity of differentiating probabilistic reasoning during training. The authors prove that while approximating these gradients is generally intractable, it becomes tractable with some constraints. They introduce WeightME, a gradient estimator that uses model sampling and provides unbiased estimates with logarithmic guarantees. This approach is evaluated on various benchmarks, showing improved performance compared to biased approximations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve the training process of probabilistic neurosymbolic models. These models combine neural networks and logical reasoning to make decisions. The authors show that there are limits to how well these gradients can be approximated, but they develop a method called WeightME that can do it more accurately. They tested this method on some examples and found that it performed better than other methods. |
