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Found 6 papers, 1 papers with code
Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative
In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative.
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models
To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale. We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales.
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance.
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting.
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information
At the county level, TDEFSI outperforms the other methods.
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers.
