🤖 AI Summary
This work addresses the computational inefficiency of logic-based explainable artificial intelligence (XAI) methods in large-scale neural networks, which often suffer from high computational costs. The authors propose a novel approach that integrates bound propagation with constraint simplification, uniquely leveraging the results of bound propagation to guide the constraint simplification process. This integration effectively reduces redundant binary variables and tightens neuron value bounds, thereby significantly accelerating the generation of logic-driven explanations. Empirical evaluations demonstrate that the method achieves up to an 89.26% reduction in explanation time on large neural networks, substantially enhancing the scalability and practical applicability of logic-based XAI techniques.
📝 Abstract
Logic-based methods for explaining neural network decisions offer formal guarantees of correctness and non-redundancy, but they often suffer from high computational costs, especially for large networks. In this work, we improve the efficiency of such methods by combining bound propagation with constraint simplification. These simplifications, derived from the propagation, tighten neuron bounds and eliminate unnecessary binary variables, making the explanation process more efficient. Our experiments suggest that combining these techniques reduces explanation time by up to 89.26\%, particularly for larger neural networks.