General: Deep Learning Applications - chenyang03/Reading GitHub Wiki

Prediction

  • {Suhara17} Yoshihiko Suhara, Yinzhan Xu, Alex 'Sandy' Pentland. DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks. Proc. of WWW, 2017. forecasting severely depressed moods based on self-reported histories
  • {Ma17} Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao. Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks. Proc. of KDD, 2017. with the attention mechanisms, Dipole can interpret the prediction results effectively
  • {Feng18} Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. DeepMove: Predicting Human Mobility with Attentional Recurrent Networks. Proc. of WWW, 2018. design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility; propose a historical attention model with two mechanisms to capture the multi-level periodicity in a principle way
  • {Hu18} Wenjian Hu, Krishna Kumar Singh, Fanyi Xiao, Jinyoung Han, Chen-Nee Chuah, and Yong Jae Lee. Who Will Share My Image?: Predicting the Content Diffusion Path in Online Social Networks. Proc. of ACM WSDM, 2018. Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network
  • {Lee19} Seokjun Lee , Rhan Ha, and Hojung Cha. Click Sequence Prediction in Android Mobile Applications. IEEE Transactions on Human-Machine Systems, 2019. PathFinder, a scheme for collecting click events and based on them predicting the next click in the application.

Security and Privacy

  • {Ma16} Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, Meeyoung Cha. Detecting Rumors from Microblogs with Recurrent Neural Networks. Proc. of IJCAI, 2016. RNN-based method detects rumors more quickly and accurately than existing techniques
  • {Ruchansky17} Natali Ruchansky, Sungyong Seo, and Yan Liu. CSI: A Hybrid Deep Model for Fake News Detection. Proc. of ACM CIKM, 2017. characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it | Recurrent Neural Network to capture the temporal pattern of user activity on a given article
  • {Yao17} Yuanshun Yao, Bimal Viswanath, Jenna Cryan, Haitao Zheng, Ben Y. Zhao. Automated Crowdturfing A￿acks and Defenses in Online Review Systems. Proc. of ACM CCS, 2017. identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks) to automate the generation of fake online reviews for products and services
  • {Amini18} Sara Amini, Vahid Noroozi, Amit Pande, Satyajit Gupte, Philip S. Yu, Chris Kanich. DeepAuth: A Framework for Continuous User Re-authentication in Mobile Apps. Proc. of ACM CIKM, 2018. a LSTM-based authentication framework, called DeepAuth, that leverages a user’s passive behavior while shopping online to continuously re-authenticate the user, providing security without compromising usability
  • {Gong18} Qingyuan Gong, Yang Chen, Xinlei He, Zhou Zhuang, Tianyi Wang, Hong Huang, Xin Wang, Xiaoming Fu. DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks. IEEE Communications Magazine, 2018, 56(11):21-27.

Modeling

  • {Yang17} Cheng Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, and Edward Y. Chang. A Neural Network Approach to Jointly Modeling Social Networks and Mobile Trajectories. ACM Transactions on Information Systems, 2017, 35(4): Article 36. develop a joint approach to model LBSN data by characterizing both the social network and mobile trajectory data