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Found 11 papers, 5 papers with code
Distance Metric Learning with Joint Representation Diversification
Distance metric learning (DML) is to learn a representation space equipped with a metric, such that examples from the same class are closer than examples from different classes with respect to the metric.
MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering
Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality.
SpotTune: Leveraging Transient Resources for Cost-efficient Hyper-parameter Tuning in the Public Cloud
Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms.
CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.
StageNet: Stage-Aware Neural Networks for Health Risk Prediction
Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets.
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics.
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability.
MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones
Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size.
Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions
However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs.
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i. e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i. e., data collection costs) for ensuring a certain level of quality.
Motif-based Rule Discovery for Predicting Real-valued Time Series
Time series prediction is of great significance in many applications and has attracted extensive attention from the data mining community.
