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Found 5 papers, 2 papers with code
Improving Deep Embedded Clustering via Learning Cluster-level Representations
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.
Data-Driven Moving Horizon Estimation Using Bayesian Optimization
In this work, an innovative data-driven moving horizon state estimation is proposed for model dynamic-unknown systems based on Bayesian optimization.
Enhancing Control Performance through ESN-Based Model Compensation in MPC for Dynamic Systems
Deriving precise system dynamic models through traditional numerical methods is often a challenging endeavor.
Label-dependent and event-guided interpretable disease risk prediction using EHRs
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.
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records
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.
