• Corpus ID: 53436447

A Comparative Study of Rule Extraction for Recurrent Neural Networks

@article{Wang2018ACS,
  title={A Comparative Study of Rule Extraction for Recurrent Neural Networks},
  author={Qinglong Wang and Kaixuan Zhang and Alexander Ororbia and Xinyu Xing and Xue Liu and C. Lee Giles},
  journal={arXiv: Learning},
  year={2018}
}
Understanding recurrent networks through rule extraction has a long history. This has taken on new interests due to the need for interpreting or verifying neural networks. One basic form for representing stateful rules is deterministic finite automata (DFA). Previous research shows that extracting DFAs from trained second-order recurrent networks is not only possible but also relatively stable. Recently, several new types of recurrent networks with more complicated architectures have been… 
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References

SHOWING 1-10 OF 50 REFERENCES

Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review

This article reviews the progress of techniques for extraction of rules from RNNs and develops a taxonomy specifically designed for this purpose, and identifies important open research issues that can give the field a significant push forward.

Symbolic Knowledge Representation in Recurrent Neural Networks: Insights from Theoretical Models of

This chapter addresses some fundamental issues in regard to recurrent neural network architectures and learning algorithms, their computational power, their suitability for diierent classes of applications, and their ability to acquire symbolic knowledge through learning.

Inducing Regular Grammars Using Recurrent Neural Networks

This work trains a recurrent neural network to distinguish between strings that are in or outside a regular language, and utilizes an algorithm for extracting the learned finite-state automaton.

Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks

It is shown that a recurrent, second-order neural network using a real-time, forward training algorithm readily learns to infer small regular grammars from positive and negative string training samples, and many of the neural net state machines are dynamically stable, that is, they correctly classify many long unseen strings.

An Empirical Evaluation of Recurrent Neural Network Rule Extraction

It is shown that production rules can be stably extracted from trained RNNs and that in certain cases the rules outperform the trained Rnns.

Second-order recurrent neural networks for grammatical inference

It is shown that a recurrent, second-order neural network using a real-time, feedforward training algorithm readily learns to infer regular grammars from positive and negative string training samples and many of the neural net state machines are dynamically stable and correctly classify long unseen strings.

An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks

It is shown that production rules can be stably extracted from trained RNNs and that in certain cases, the rules outperform the trained Rnns.

Learning long-term dependencies in NARX recurrent neural networks

It is shown that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models with exogenous (NARX) recurrent neural networks, which have powerful representational capabilities.

The induction of dynamical recognizers

A longitudinal examination of the learning process of a higher order recurrent neural network architecture illustrates a new form of mechanical inference: Induction by phase transition, and a hypothesis relating linguistic generative capacity to the behavioral regimes of non-linear dynamical systems is concluded.

Active Grammatical Inference: A New Learning Methodology

A new methodology for inferring regular grammars y from a set of positive and negative examples and a-priori knowledge (if available) is presented, that is based on a combination of neural and symbolic techniques, which can be guided by the validated previous results and/or by introducing constraints or (positive/negative) rules dynamically.