Search Results for author: Yasha Wang

Found 11 papers, 5 papers with code

Distance Metric Learning with Joint Representation Diversification

1 code implementation ICML 2020 Xu Chu, Yang Lin, Xiting Wang, Xin Gao, Qi Tong, Hailong Yu, Yasha Wang

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.

Metric Learning

MedFACT: Modeling Medical Feature Correlations in Patient Health Representation Learning via Feature Clustering

no code implementations21 Apr 2022 Xinyu Ma, Xu Chu, Yasha Wang, Hailong Yu, Liantao Ma, Wen Tang, Junfeng Zhao

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.

Representation Learning

StageNet: Stage-Aware Neural Networks for Health Risk Prediction

1 code implementation24 Jan 2020 Junyi Gao, Cao Xiao, Yasha Wang, Wen Tang, Lucas M. Glass, Jimeng Sun

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

1 code implementation27 Nov 2019 Liantao Ma, Chaohe Zhang, Yasha Wang, Wenjie Ruan, Jiantao Wang, Wen Tang, Xinyu Ma, Xin Gao, Junyi Gao

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

1 code implementation27 Nov 2019 Liantao Ma, Junyi Gao, Yasha Wang, Chaohe Zhang, Jiangtao Wang, Wenjie Ruan, Wen Tang, Xin Gao, Xinyu Ma

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.

Representation Learning

MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones

1 code implementation18 Mar 2019 Junyi Gao, Weihao Tan, Liantao Ma, Yasha Wang, Wen Tang

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-Task Learning

Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions

no code implementations1 Nov 2018 Xu Chu, Yang Lin, Jingyue Gao, Jiangtao Wang, Yasha Wang, Leye Wang

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

no code implementations19 Apr 2018 Leye Wang, wenbin liu, Daqing Zhang, Yasha Wang, En Wang, Yongjian Yang

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.

reinforcement-learning Transfer Learning

Motif-based Rule Discovery for Predicting Real-valued Time Series

no code implementations14 Sep 2017 Yuanduo He, Xu Chu, Juguang Peng, Jingyue Gao, Yasha Wang

Time series prediction is of great significance in many applications and has attracted extensive attention from the data mining community.

Time Series Time Series Prediction

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