Search Results for author:
Found 5 papers, 2 papers with code
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U. S. Government has been making a concerted effort to ensure American leadership in AI.
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation
In recent years, emerging fields such as meta-learning or self-supervised learning have been closing the gap between proof-of-concept results and real-life applications of machine learning by extending deep-learning to the semi-supervised and few-shot domains.
Spectral PINNs: Fast Uncertainty Propagation with Physics-Informed Neural Networks
We aim to quickly quantify the impact of uncertain parameters onto the solution of a PDE - that is - we want to perform fast uncertainty propagation.
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
Climate models project an uncertainty range of possible warming scenarios from 1. 5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble.
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
To help address this problem, we introduce the Storm EVent ImagRy (SEVIR) dataset - a single, rich dataset that combines spatially and temporally aligned data from multiple sensors, along with baseline implementations of deep learning models and evaluation metrics, to accelerate new algorithmic innovations.
Ranked #6 on
Weather Forecasting
on SEVIR
