Summary of Extracting Formulae in Many-valued Logic From Deep Neural Networks, by Yani Zhang et al.
Extracting Formulae in Many-Valued Logic from Deep Neural Networks
by Yani Zhang, Helmut Bölcskei
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
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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 paper proposes a new perspective on deep ReLU networks by viewing them as circuit counterparts of Lukasiewicz infinite-valued logic, a many-valued generalization of Boolean logic. The authors develop an algorithm for extracting formulae in MV logic from deep ReLU networks, which can be applied to networks with general weights, including real-valued weights. This allows the extraction of logical formulae from deep ReLU networks trained on data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computer networks work by looking at them like special kinds of logic circuits. It’s a new way to think about how these networks process information. The researchers created an algorithm that can take a network and turn it into a logical formula, which is a set of rules for making decisions. This could be useful for training networks with real-world data. |
Keywords
* Artificial intelligence * Generalization * Relu
