26 citations · 38 across the 5 of their papers we have counts for
7 papers
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…
Rescene: stable century-long climate emulation from a frozen weather model
Minjong Cheon
Machine-learning weather models now match or exceed operational numerical prediction in the medium range, but when they are run freely for years they drift, blow up or lose their v…
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…
Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks
Minjong Cheon
In the realm of deep learning, the Kolmogorov-Arnold Network (KAN) has emerged as a potential alternative to multilayer projections (MLPs). However, its applicability to vision tas…
Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing
Minjong Cheon
In this research, we propose the first approach for integrating the Kolmogorov-Arnold Network (KAN) with various pre-trained Convolutional Neural Network (CNN) models for remote se…