State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Recurrent neural networks (RNNs) are powerful sequence models but suffer from poor interpretability and limited long-term memorization.
• Existing RNNs often function as black boxes, hindering understanding of their decision-making processes.
• The inherent capacity for long-term memory in RNNs is often underutilized in practice.

Purpose of the Study:

• To introduce a novel state-regularization mechanism for recurrent neural networks.
• To address the shortcomings of interpretability and long-term memorization in RNNs.
• To enhance the structural memory and explainability of RNNs.

Main Methods:

• Developed a state-regularization technique for RNNs, inducing stochastic state transitions between learnable states.
• Evaluated state-regularized RNNs on regular and non-regular language tasks, including automata extraction and sequence learning.
• Applied the method to real-world tasks: sentiment analysis, visual object recognition, and text categorization.

Main Results:

• State-regularization simplifies the extraction of finite state automata from RNN dynamics.
• The approach encourages RNNs to function more like automata with external memory, potentially improving structural memory.
• Demonstrated enhanced interpretability and explainability through the probabilistic finite state transition mechanism.

Conclusions:

• State-regularization offers a promising approach to improve the interpretability and memory capabilities of recurrent neural networks.
• The method facilitates the extraction of meaningful automata and enhances understanding of RNN behavior.
• State-regularized RNNs show potential for more robust and explainable sequence learning across diverse applications.