  Recently, deep models have had considerable success in several tasks,
especially with low-level representations. However, effective learning from
sparse noisy samples is a major challenge in most deep models, especially in
domains with structured representations. Inspired by the proven success of
human guided machine learning, we propose Knowledge-augmented Column Networks,
a relational deep learning framework that leverages human advice/knowledge to
learn better models in presence of sparsity and systematic noise.
