Explore the Disentanglement Mechanism for Deep Learning
Haiquan Qiu, Quanming Yao · 2025
Deep learning's success is accompanied by challenges in interpretability and efficiency. This research explores disentanglement mechanisms in deep learning across three core dimensions: model expressivity, optimization paradigms, and interpretability. We first analyze how Graph Neural Networks learn logical rules through representation disentanglement, establishing theoretical foundations for their expressivity. We then develop efficient parameter merging strategies by leveraging feature decomposition in network parameters. Finally, we design neural architectures aligned with symbolic formulas for modeling complex network dynamics, enhancing transparency through architecture disentanglement. Our research provides both theoretical insights and practical methodologies for building more interpretable and efficient AI systems. Future work will further advance these directions through precision-focused expressivity analysis, semantic-aware parameter optimization, and crossdomain applications of interpretable modeling.