Summary of Distilling Symbolic Priors For Concept Learning Into Neural Networks, by Ioana Marinescu et al.
Distilling Symbolic Priors for Concept Learning into Neural Networks
by Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers explore the idea of instilling artificial neural networks with the same ability to learn new concepts quickly as humans do. They achieve this by distilling a prior distribution from a symbolic Bayesian model using a technique called meta-learning. This approach enables them to transfer the inductive biases from the Bayesian model into a neural network. The authors demonstrate their method by creating a neural network that can learn logical formulas from just a few examples, and show that its performance aligns closely with human results in behavioral experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how artificial neural networks can be made to learn new concepts quickly, like humans do. Scientists used a special technique called meta-learning to take the knowledge from an old model and put it into a new one. They showed this new network can learn simple formulas by looking at just a few examples, and it does it in a way that’s very similar to how people learn. |
Keywords
* Artificial intelligence * Meta learning * Neural network
