4 citations · 5 across the 6 of their papers we have counts for
8 papers
Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts
Fanny Lehmann, Firat Ozdemir, Yun Cheng +4
While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons. This…
Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
Firat Ozdemir, Yun Cheng, Salman Mohebi +11
Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets to enable versatile downstream applications through…
Finetuning a Weather Foundation Model with Lightweight Decoders for Unseen Physical Processes
Fanny Lehmann, Firat Ozdemir, Benedikt Soja +3
Recent advances in AI weather forecasting have led to the emergence of so-called "foundation models", typically defined by expensive pretraining and minimal fine-tuning for downstr…
FocusDD: Real-World Scene Infusion for Robust Dataset Distillation
Youbing Hu, Yun Cheng, Olga Saukh +4
Dataset distillation has emerged as a strategy to compress real-world datasets for efficient training. However, it struggles with large-scale and high-resolution datasets, limiting…
Retrospective Uncertainties for Deep Models using Vine Copulas
Nataša Tagasovska, Firat Ozdemir, Axel Brando
Despite the major progress of deep models as learning machines, uncertainty estimation remains a major challenge. Existing solutions rely on modified loss functions or architectura…
OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
Firat Ozdemir, Berkan Lafci, Xosé Luís Deán-Ben +2
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by subsequent detection of ultrasound waves generated via ligh…