Search Results for author:
Found 2 papers, 1 papers with code
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms.
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
