PreBoot-SI: Enhancing Structural Inference with Automatically Learned Structural Priors
Zhongben Gong, Xiaoqun Wu, Mingyang Zhou
We propose PreBoot-SI, a general bootstrapped framework for structural inference in interacting dynamical systems. It integrates a pretrained structural estimator and a joint inference module based on the Variational Autoencoder (VAE); these components are alternately updated to progressively refine the inferred structures. Initially, the structural estimator is trained on labels from either a meta-dataset or a baseline model to extract features and generate structural priors, which provide multi-level guidance for training the joint inference module. In subsequent iterations, pseudo-labels from the joint module replace the initial labels. PreBoot-SI is compatible with various VAE-based models. Experiments on synthetic datasets of physical systems demonstrate that PreBoot-SI significantly enhances the performance of structural inference models such as Neural Relational Inference (NRI). Ablation studies reveal that feature and structural prior inputs to the joint module offer complementary improvements from representational and generative perspectives.