Incorporating Domain Knowledge into Deep Neural Networks
APP Centre for Artificial Intelligence Research (APPCAIR)（人工智能研究中心）；Department of Electrical and Electronics Engineering（电子与电气工程系）；Department of Computer Science and Information Systems（计算机科学与信息系统系）；BITS Pilani, K.K. Birla Goa Campus（比斯汉学院，戈亚校区）
We present a survey of ways in which domain-knowledge has been included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines two broad approaches to encode such knowledge--as logical and numerical constraints--and describes techniques and results obtained in several sub-categories under each of these approaches.
Incorporating planning intelligence into deep learning: A planning support tool for street network design
Martin Centre for Architectural and Urban Studies, University of Cambridge（剑桥大学马丁建筑与城市研究中心）；Department of Architecture, University of Cambridge（剑桥大学建筑系）
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
Southwestern University of Finance and Economics（西南财经大学）；Inboc Technologies Ltd.（Inboc科技有限公司）；University of Edinburgh（爱丁堡大学）；ArrayStream Technologies Ltd.（ArrayStream科技有限公司）；University of Glasgow（格拉斯哥大学）
