26 citations · 38 across the 5 of their papers we have counts for
5 papers · 1 filter
Sonny-ENS: demystifying machine-learning ensemble weather forecasting
Minjong Cheon
Every weather forecast is uncertain, and decisions from flood warnings to energy trading depend on how well that uncertainty is quantified. Machine-learning (ML) ensembles now quan…
Low-Rank Kolmogorov-Arnold Generator Flows for Interpretable Operator Learning
Minjong Cheon
Neural operators that learn physical laws from data should generalize beyond their training distribution and expose the learned law in closed form. The Kolmogorov-Arnold neural ope…
KNOU-Flood: Advancing global streamflow forecasting with lead-aware probabilistic machine learning
Minjong Cheon
Global streamflow forecasting supports flood preparedness, yet data-driven models trained on reanalysis lose skill when driven by operational forecasts, increasingly so with lead t…
Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE
Minjong Cheon
Deep learning weather prediction models achieve remarkable predictive skill yet remain largely opaque: we know little about how they represent physical climate phenomena internally…
KAN-CL: Per-Knot Importance Regularization for Continual Learning with Kolmogorov-Arnold Networks
Minjong Cheon
Catastrophic forgetting remains the central obstacle in continual learning (CL): parameters shared across tasks interfere with one another, and existing regularization methods such…