Scalable Approach to Differentiable Pattern Mining with Coverage Regularization

This paper presents a novel scalable approach to differentiable pattern mining that incorporates coverage regularization. The method enables end-to-end learning of pattern mining tasks while maintaining computational efficiency on large-scale datasets. Our approach demonstrates significant improvements over traditional pattern mining techniques by leveraging modern deep learning optimization strategies.

Conference paper

Pacific-Asia Conference on Knowledge Discovery and Data Mining