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CAN:Effective Cross Features by global attention mechanism and Neural Network for Ad Click Prediction

Tsinghua Science and Technology 2022年第1期27卷 186-195页

作者： Wenjie Cai Yufeng Wang Jianhua Ma Qun Jin Nanjing University of Posts and Telecommunications(NJUPT) Nanjing210003China Digital Media Department in the Faculty of Computer and Information Sciences Hosei UniversityTokyo 163-8001Japan Networked Information Systems Laboratory Department of Human Informatics and Cognitive SciencesWaseda UniversityTokyo 163-8001Japan

Online advertising click-through rate(CTR) prediction is aimed at predicting the probability of a user clicking an ad,and it has undergone considerable development in recent *** of the hot topics in this area is the construction of feature interactions to facilitate accurate *** machine provides second-order feature interactions by linearly multiplying hidden feature ***,real-world data present a complex and nonlinear ***,second-order feature interactions are unable to represent cross information *** drawback has been addressed using deep neural networks(DNNs),which enable high-order nonlinear feature ***,DNN-based feature interactions cannot easily optimize deep structures because of the absence of cross information in the original *** this study,we propose an effective CTR prediction algorithm called CAN,which explicitly exploits the benefits of attention mechanisms and DNN *** attention mechanism is used to provide rich and expressive low-order feature interactions and facilitate the optimization of DNN-based predictors that implicitly incorporate high-order nonlinear feature *** experiments using two real datasets demonstrate that our proposed CAN model performs better than other cross feature-and DNN-based predictors.

关键词： click-through rate prediction global attention mechanism feature interaction neural network

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Modified YOLO network for symptom detection in panoramic oral roentgenogram

Modified YOLO network for symptom detection in panoramic ora...

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第43届中国控制会议

作者： Yinggui Huang Qinqin Chai Wu Wang College of Electrical Engineering and Automation Fuzhou University

In this paper,a dental symptom detection model based on YOLO is proposed in order to detect different dental symptom in panoramic oral *** model introduces the global attention mechanism into the backbone feature extraction network to obtain rich cross-latitude features and enhance the network's global feature extraction capabilities in low-contrast *** the same time,the Spatial Pyramid Pooling Fast module in the network is replaced and the Atrous Spatial Pyramid Pooling technology is used to improve the recognition ability of larger targets such as tooth ***,according to the special structure,size and position of different dental symptoms,the Focal-EIoU is introduced to replace CIoU,which increases the weight proportion of positive samples in the training process and reduces the problem of missed detection or false *** on self-built data sets show that the improved YOLO model has improved mAP@0.5 by 4.3%compared to the original model,and the detection effect has been generally improved.

关键词： Focal-EIoU data enhancement Defect detection global attention mechanism YOLO

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