Summary of Neural Symbolic Logical Rule Learner For Interpretable Learning, by Bowen Wei and Ziwei Zhu
Neural Symbolic Logical Rule Learner for Interpretable Learning
by Bowen Wei, Ziwei Zhu
First submitted to arxiv on: 21 Aug 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 The paper introduces the Normal Form Rule Learner (NFRL) algorithm, a selective discrete neural network that learns rules in Conjunctive Normal Form (CNF) and Disjunctive Normal Form (DNF) for enhanced accuracy and interpretability. The NFRL architecture incorporates two specialized Normal Form Layers (NFLs), a Negation Layer, and a Normal Form Constraint (NFC) to streamline neuron connections. The model is optimized using adaptive gradient updates with the Straight-Through Estimator to overcome the gradient vanishing challenge. Experimental results on 11 datasets show that NFRL outperforms 12 state-of-the-art alternatives in terms of classification performance, quality of learned rules, efficiency, and interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for computers to learn from data using special rules. The “Normal Form Rule Learner” algorithm is designed to be easy to understand and work with. It uses a special kind of neural network that can learn rules in different formats. This helps the model make better predictions and decisions. The authors tested their method on many datasets and found it performed well compared to other methods. |
Keywords
» Artificial intelligence » Classification » Neural network
