Search Results for author:
Found 15 papers, 2 papers with code
Multi-View Factorization Machines
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.
dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction
Mood disorders are common and associated with significant morbidity and mortality.
Human-Computer Interaction Computers and Society
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
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.
Broad Learning for Healthcare
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).
Learning from Multi-View Multi-Way Data via Structural Factorization Machines
Real-world relations among entities can often be observed and determined by different perspectives/views.
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
On electronic game platforms, different payment transactions have different levels of risk.
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment
In this paper, we study unsupervised feature selection for multi-view data, as class labels are usually expensive to obtain.
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
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.
A review of heterogeneous data mining for brain disorders
Brain disorder data poses many unique challenges for data mining research.
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Network analysis of human brain connectivity is critically important for understanding brain function and disease states.
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
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.
Deep Learning Towards Mobile Applications
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.
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
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.
Private Model Compression via Knowledge Distillation
To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
