Search Results for author:
Found 3 papers, 2 papers with code
TimeX++: Learning Time-Series Explanations with Information Bottleneck
The design of the objective function builds upon the principle of information bottleneck (IB), and modifies the IB objective function to avoid trivial solutions and distributional shift issues.
FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation
FIDLAR seamlessly integrates two neural network modules: one called the Flood Manager, which is responsible for generating water pre-release schedules, and another called the Flood Evaluator, which assesses these generated schedules.
Deep Learning Models for Flood Predictions in South Florida
To overcome this problem, we train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
