no code implementations • 13 Nov 2018 • Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu
To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
no code implementations • 11 Sep 2018 • Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.
no code implementations • 10 Sep 2018 • Ji Wang, Jian-Guo Zhang, Weidong Bao, Xiaomin Zhu, Bokai Cao, Philip S. Yu
To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers.
no code implementations • 10 Sep 2018 • Ji Wang, Bokai Cao, Philip S. Yu, Lichao Sun, Weidong Bao, Xiaomin Zhu
In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.
1 code implementation • 29 Aug 2018 • He Huang, Bokai Cao, Philip S. Yu, Chang-Dong Wang, Alex D. Leow
Mood disorders are common and associated with significant morbidity and mortality.
Human-Computer Interaction Computers and Society
no code implementations • 19 Jun 2018 • Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin, Alex D. Leow
Network analysis of human brain connectivity is critically important for understanding brain function and disease states.
no code implementations • 23 Mar 2018 • Bokai Cao, Lei Zheng, Chenwei Zhang, Philip S. Yu, Andrea Piscitello, John Zulueta, Olu Ajilore, Kelly Ryan, Alex D. Leow
The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives.
no code implementations • 23 Mar 2018 • Bokai Cao
A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e. g., clinical measures collected at hospitals), tensor data (e. g., neuroimages analyzed by research institutes), graph data (e. g., brain connectivity networks), and sequence data (e. g., digital footprints recorded on smart sensors).
no code implementations • 13 Sep 2017 • Bokai Cao, Mia Mao, Siim Viidu, Philip S. Yu
On electronic game platforms, different payment transactions have different levels of risk.
no code implementations • 2 May 2017 • Xiaokai Wei, Bokai Cao, Philip S. Yu
In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain.
no code implementations • 10 Apr 2017 • Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao, Philip S. Yu
Real-world relations among entities can often be observed and determined by different perspectives/views.
no code implementations • 19 Aug 2015 • Bokai Cao, Xiangnan Kong, Jingyuan Zhang, Philip S. Yu, Ann B. Ragin
In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views.
no code implementations • 5 Aug 2015 • Bokai Cao, Xiangnan Kong, Philip S. Yu
Brain disorder data poses many unique challenges for data mining research.
1 code implementation • 3 Jun 2015 • Bokai Cao, Hucheng Zhou, Guoqiang Li, Philip S. Yu
In this paper, we propose a general predictor, named multi-view machines (MVMs), that can effectively include all the possible interactions between features from multiple views.
no code implementations • 20 May 2013 • Xiangnan Kong, Bokai Cao, Philip S. Yu, Ying Ding, David J. Wild
Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.