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Found 4 papers, 1 papers with code
The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation
Floods can cause horrific harm to life and property.
Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems.
Explainable Parallel RCNN with Novel Feature Representation for Time Series Forecasting
Accurate time series forecasting is a fundamental challenge in data science.
Cache Replacement as a MAB with Delayed Feedback and Decaying Costs
We derive an optimal learning rate for EXP4-DFDC that defines the balance between exploration and exploitation and proves theoretically that the expected regret of our algorithm is a vanishing quantity as a function of time.
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