Human-Knowledge Integrated Methods for Learning With Small-Data Challenges

The recent success of artificial intelligence can partly be attributed to advancements in data-driven learning approaches, increased data availability, and enhanced computational power. However, in scenarios where data are hard to collect and the learning task demands complex domain-specific knowledge — for example, in biomedical applications — purely data-driven approaches may yield inappropriate and uninterpretable results. These outcomes sometimes contradict natural or human rules and may even raise ethical concerns. Incorporating human or expert knowledge, such as extra annotations, human-defined rules, and domain-specific engineering, can significantly enhance representation learning on small datasets. A major challenge in this context is that much of human knowledge cannot be directly represented as numerical values, making it difficult for models to effectively utilize this information.

Ph.D. Dissertation, Brandeis University