Knowledge-augmented Column Networks：用建议指导深度学习
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice
摘要：
最近，深度模型在几个任务中取得了相当大的成功，特别是在低级表示方面。然而，在大多数深度模型中，从稀疏噪声样本中进行有效学习是一个主要挑战，特别是在具有结构化表示的领域。受人类引导的机器学习的成功经验的启发，我们提出了知识增强的列网络，这是一个关系型深度学习框架，它利用人类的建议/知识在稀疏性和系统噪声的情况下学习更好的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
