A naukri.com initiative
Arxiv
1w
105
Image Credit: Arxiv
An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
- Deep learning models in regression tasks face challenges due to missing instances in time series data.
- Monte Carlo Temporal Dropout (MC-TD) is introduced to address input-level uncertainty in Earth Observation time series data.
- Monte Carlo Concrete Temporal Dropout (MC-ConcTD) learns the optimal dropout distribution to improve predictive performance.
- Experiments demonstrate that MC-ConcTD enhances predictive performance and uncertainty calibration for EO applications.
Read Full Article
6 Likes
For uninterrupted reading, download the app