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.
no code implementations • 15 Apr 2024 • Yang Lin, Xinyu Ma, Xu Chu, Yujie Jin, Zhibang Yang, Yasha Wang, Hong Mei
We then demonstrate the theoretical mechanism of our LoRA Dropout mechanism from the perspective of sparsity regularization by providing a generalization error bound under this framework.
1 code implementation • 8 Mar 2024 • Weibin Liao, Yinghao Zhu, Xinyuan Wang, Chengwei Pan, Yasha Wang, Liantao Ma
This highlights the potential of Mamba in facilitating model lightweighting.
no code implementations • 30 Jan 2024 • Weibin Liao, Yinghao Zhu, Zixiang Wang, Xu Chu, Yasha Wang, Liantao Ma
PAI no longer introduces any imputed data but constructs a learnable prompt to model the implicit preferences of the downstream model for missing values, resulting in a significant performance improvement for all EHR analysis models.
1 code implementation • 18 Jan 2024 • Ruizhe Zhang, Xinke Jiang, Yuchen Fang, Jiayuan Luo, Yongxin Xu, Yichen Zhu, Xu Chu, Junfeng Zhao, Yasha Wang
Graph Neural Networks (GNNs) have shown considerable effectiveness in a variety of graph learning tasks, particularly those based on the message-passing approach in recent years.
1 code implementation • 14 Jan 2024 • Zhihao Yu, Xu Chu, Liantao Ma, Yasha Wang, Wenwu Zhu
To bridge this gap, we propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series.
1 code implementation • 28 Dec 2023 • Zhihao Yu, Liantao Ma, Yasha Wang, Junfeng Zhao
In particular, a hierarchical convolution structure is introduced to extract the information from the series at various scales.
no code implementations • 26 Dec 2023 • Xinke Jiang, Ruizhe Zhang, Yongxin Xu, Rihong Qiu, Yue Fang, Zhiyuan Wang, Jinyi Tang, Hongxin Ding, Xu Chu, Junfeng Zhao, Yasha Wang
In this paper, we investigate the retrieval-augmented generation (RAG) based on Knowledge Graphs (KGs) to improve the accuracy and reliability of Large Language Models (LLMs).
1 code implementation • 18 Dec 2023 • Zhihao Yu, Chaohe Zhang, Yasha Wang, Wen Tang, Jiangtao Wang, Liantao Ma
Predicting health risks from electronic health records (EHR) is a topic of recent interest.
no code implementations • 11 Oct 2023 • Zhongji Zhang, Yuhang Wang, Yinghao Zhu, Xinyu Ma, Tianlong Wang, Chaohe Zhang, Yasha Wang, Liantao Ma
Due to the limited information about emerging diseases, symptoms are hard to be noticed and recognized, so that the window for clinical intervention could be ignored.
no code implementations • 4 Oct 2023 • Hongxin Ding, Peinie Zou, Zhiyuan Wang, Junfeng Zhao, Yasha Wang, Qiang Zhou
Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making.
1 code implementation • 17 Jan 2023 • Liantao Ma, Chaohe Zhang, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Xinyu Ma, Yasha Wang, Wen Tang, Xinju Zhao, Wenjie Ruan, Tao Wang
Here, our objective is to develop a deep learning model for a real-time, individualized, and interpretable mortality prediction model - AICare.
1 code implementation • 28 Oct 2022 • Yujie Jin, Xu Chu, Yasha Wang, Wenwu Zhu
Based on the proposed term of invariance, we propose a novel deep DG method called Angular Invariance Domain Generalization Network (AIDGN).
1 code implementation • 28 Oct 2022 • Chaohe Zhang, Xu Chu, Liantao Ma, Yinghao Zhu, Yasha Wang, Jiangtao Wang, Junfeng Zhao
M3Care is an end-to-end model compensating the missing information of the patients with missing modalities to perform clinical analysis.
3 code implementations • 16 Sep 2022 • Junyi Gao, Yinghao Zhu, Wenqing Wang, Yasha Wang, Wen Tang, Ewen M. Harrison, Liantao Ma
Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data.
no code implementations • 21 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.
no code implementations • 7 Dec 2020 • Yan Li, Bo An, Junming Ma, Donggang Cao, Yasha Wang, Hong Mei
Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms.
no code implementations • 17 Jul 2020 • Liantao Ma, Xinyu Ma, Junyi Gao, Chaohe Zhang, Zhihao Yu, Xianfeng Jiao, Wenjie Ruan, Yasha Wang, Wen Tang, Jiangtao Wang
Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide.
1 code implementation • 24 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.
1 code implementation • 27 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.
1 code implementation • 27 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.
1 code implementation • 18 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.
no code implementations • 1 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.
no code implementations • 19 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.
no code implementations • 14 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.