Learning Proxy

Learning proxies involve using machine learning models to approximate complex or computationally expensive processes, thereby enabling faster and more efficient analysis. Current research focuses on developing accurate and stable proxy models for diverse applications, including climate modeling (using neural networks to emulate cloud microphysics), deep metric learning (leveraging visual prompts to improve parameter-efficient fine-tuning), and representing complex concepts like culture in large language models. The development of reliable learning proxies offers significant potential for accelerating scientific discovery and improving the performance of various applications by replacing computationally intensive simulations or analyses with faster, learned approximations.