A Free Knob: Decoupling Calibration and Predictive Skill in Threshold-Based Evaluation

arXiv:2609.33457 · cs.LG · Submitted 2026-09-27 · Read on arXiv

cs.LG

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/munim110/free-knob

Terminology

• Calibration — 72× in this paper · explained in 7 episodes
• Verification — 17× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• In International Conference on Learning Representations — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Critical Success Index — 2× in this paper
• Decoupling — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Bangladesh University of Engineering and Technology — 1× in this paper
• Data Assimilation — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Towards Fair Comparisons of AI- and Physics-Based Weather Models for Extreme Events via the Weighted Potential CRPS
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Microsoft COCO: Common Objects in Context
• Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model
• RainDiff: End-to-end Precipitation Nowcasting Via Token-wise Attention Diffusion

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