To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
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.
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.
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.
Mood disorders are common and associated with significant morbidity and mortality.
Human-Computer Interaction Computers and Society
Network analysis of human brain connectivity is critically important for understanding brain function and disease states.
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.
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).
On electronic game platforms, different payment transactions have different levels of risk.
In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain.
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.
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.
Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.