This research introduces a framework for automatically integrating human knowledge into machine learning models without requiring manual algorithm design.

• Transforms knowledge from natural language into machine-usable representations
• Enables more robust learning in low-data environments
• Bridges the gap between human expertise and algorithmic implementation
• Creates data-efficient models with enhanced performance

Medical Impact: In healthcare settings with limited or noisy patient data, this approach could dramatically improve diagnostic and predictive models by incorporating medical expertise directly into algorithms without specialized ML engineering.

Towards Automated Knowledge Integration From Human-Interpretable Representations