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arXiv Machine Learning By Tengwei Song, Robert Hoehndorf

Soft Ontology Priors over Latent Concept Posteriors for Weakly Supervised Neuro-Symbolic Learning

Read the original on arXiv Machine Learning →

The paper introduces a method that injects a soft ontology prior into the training objective of weakly supervised neuro-symbolic predictors, aiming to prevent reasoning shortcuts that make latent concepts unidentifiable. By compiling the prior with the task constraint into a smooth probabilistic circuit, the authors show that the resulting structured concept posterior can be efficiently computed and that noisy expert axioms only redistribute prior mass without eliminating valid concept support. Theoretical analysis demonstrates bounded log‑density perturbations under axiom noise, while experiments on a real‑world driving benchmark and controlled tasks validate the approach.

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