Paper:
While extremely successful in several applications, especially with low-level representations; 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 how our approach leads to either superior overall performance or faster convergence.

Review:
This paper formulates a new method called human-guided column networks to handle sparse and noisy samples. Their main idea is to introduce human knowledge to guide the previous column network for robust training.
Pros:
1. The authors find a fresh direction for learning with noisy samples. The human advice can be viewed as previledged information.
2. The authors perform numerical experiments to demonstrate the efficacy of their framework. And their experimental result support their previous claims.
Cons:
We have three questions in the following.
1. Motivation: The authors are encouraged to re-write their paper with more motivated storyline. The current version is okay but not very exciting for idea selling. For example, human guidance should be your selling point, and you may not restrict your general method into ColumnNet, which will limit the practical usage.
2. Related works: In deep learning with noisy labels, there are three main directions, including small-loss trick [1], estimating noise transition matrix [2,3], and explicit and implicit regularization [4]. I would appreciate if the authors can survey and compare more baselines in their paper instead of listing some basic ones.
3. Experiment: 
3.1 Baselines: For noisy labels, the authors should add MentorNet [1] as a baseline https://github.com/google/mentornet From my own experience, this baseline is very strong.
3.2 Datasets: For datasets, I think the author should first compare their methods on symmetric and aysmmetric noisy data. Besides, the authors are encouraged to conduct 1 NLP dataset.
By the way, if your human guidance is totally wrong, how your model handle such extreme cases? Could you please discuss this important point in your paper?
References:
[1] L. Jiang, Z. Zhou, T. Leung, L. Li, and L. Fei-Fei. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In ICML, 2018.
[2] G. Patrini, A. Rozza, A. Menon, R. Nock, and L. Qu. Making deep neural networks robust to label noise: A loss correction approach. In CVPR, 2017.
[3] J. Goldberger and E. Ben-Reuven. Training deep neural-networks using a noise adaptation layer. In ICLR, 2017.
[4] T. Miyato, S. Maeda, M. Koyama, and S. Ishii. Virtual adversarial training: A regularization method for supervised and semi-supervised learning. ICLR, 2016.