  Recently, deep models have been successfully applied in several applications,
especially with low-level representations. However, sparse, noisy samples and
structured domains (with multiple objects and interactions) are some of the
open challenges in most deep models. Column Networks, a deep architecture, can
succinctly capture such domain structure and interactions, but may still be
prone to sub-optimal learning from sparse and noisy samples. Inspired by the
success of human-advice guided learning in AI, especially in data-scarce
domains, we propose Knowledge-augmented Column Networks that leverage human
advice/knowledge for better learning with noisy/sparse samples. Our experiments
demonstrate that our approach leads to either superior overall performance or
faster convergence (i.e., both effective and efficient).
