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This paper introduces a pipeline that uses transformers and embeddings to extract interpretable decision rules from clinical tabular data, translating latent biomarkers back to measurable features for better clinical decision support.
This paper introduces GRiD, a framework that uses diffusion models and reinforcement learning to generate graph-like rules (e.g., cycles, branches) for knowledge graph reasoning, addressing the limitations of existing chain-rule mining methods. Experiments on six benchmarks show competitive performance in KG completion tasks.