22 citations · 85 across the 24 of their papers we have counts for
6 papers · 1 filter
PDE foundation models are skillful AI weather emulators for the Martian atmosphere
Johannes Schmude, Sujit Roy, Liping Wang +10
We show that AI foundation models that are pretrained on numerical solutions to a diverse corpus of partial differential equations can be adapted and fine-tuned to obtain skillful…
WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks
Rajat Shinde, Christopher E. Phillips, Kumar Ankur +10
High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific appli…
Prithvi WxC: Foundation Model for Weather and Climate
Johannes Schmude, Sujit Roy, Will Trojak +26
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing numbe…
Improving Label Error Detection and Elimination with Uncertainty Quantification
Johannes Jakubik, Michael Vössing, Manil Maskey +2
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that…
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah +35
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…