Summary of Stability Analysis Of Various Symbolic Rule Extraction Methods From Recurrent Neural Network, by Neisarg Dave et al.
Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network
by Neisarg Dave, Daniel Kifer, C. Lee Giles, Ankur Mali
First submitted to arxiv on: 4 Feb 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 investigates two competing rule extraction methodologies: quantization and equivalence query. It trains RNN models on various grammars and evaluates their performance using different cell types (LSTM, GRU, O2RNN, MIRNN) and initialization seeds. The results show that the O2RNN model with quantization-based rule extraction outperforms others in terms of stability and accuracy. The study highlights the instability of equivalence query methods, particularly for partially trained RNNs, whereas quantization methods produce stable DFA rules. Additionally, the paper explores the performance of different RNN cells on various grammars, revealing that O2RNN produces stable DFA with high accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares two ways to extract rules from grammars: quantization and equivalence query. It uses many artificial neural networks (like LSTM) to learn patterns in words. The results show that one way, called O2RNN, works better than the others. This is because it’s more stable and accurate. The other way, called equivalence query, can be unstable and make mistakes sometimes. |
Keywords
* Artificial intelligence * Lstm * Quantization * Rnn
