no code implementations • COLING 2022 • Qing Yin, Zhihua Wang, Yunya Song, Yida Xu, Shuai Niu, Liang Bai, Yike Guo, Xian Yang
In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations.
no code implementations • 12 Nov 2023 • Qing Sun, Shuai Niu, Minrui Fei
In this work, an innovative data-driven moving horizon state estimation is proposed for model dynamic-unknown systems based on Bayesian optimization.
no code implementations • 12 Nov 2023 • Shuai Niu, Qing Sun, Minrui Fei, Xuqian Ju
Deriving precise system dynamic models through traditional numerical methods is often a challenging endeavor.
1 code implementation • 18 Jan 2022 • Shuai Niu, Yunya Song, Qing Yin, Yike Guo, Xian Yang
Thirdly, both label-dependent and event-guided representations are integrated to make a robust prediction, in which the interpretability is enabled by the attention weights over words from medical notes.
1 code implementation • 18 Jan 2022 • Shuai Niu, Qing Yin, Yunya Song, Yike Guo, Xian Yang
In this paper, we propose a label dependent attention model LDAM to 1) improve the interpretability by exploiting Clinical-BERT (a biomedical language model pre-trained on a large clinical corpus) to encode biomedically meaningful features and labels jointly; 2) extend the idea of joint embedding to the processing of time-series data, and develop a multi-modal learning framework for integrating heterogeneous information from medical notes and time-series health status indicators.