203 citations · 209 across the 4 of their papers we have counts for
4 papers
Beyond Calibration: Physically Informed Learning for Raw-to-Raw Mapping
Peter Grönquist, Stepan Tulyakov, Dengxin Dai
Achieving consistent color reproduction across multiple cameras is essential for seamless image fusion and Image Processing Pipeline (ISP) compatibility in modern devices, but it i…
Unlocking Comics: The AI4VA Dataset for Visual Understanding
Peter Grönquist, Deblina Bhattacharjee, Bahar Aydemir +4
In the evolving landscape of deep learning, there is a pressing need for more comprehensive datasets capable of training models across multiple modalities. Concurrently, in digital…
Deep Learning for Post-Processing Ensemble Weather Forecasts
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun +4
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which c…
Predicting Weather Uncertainty with Deep Convnets
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden +4
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations.…