Minimal Cost

"Minimal cost" research focuses on achieving significant improvements in model performance or capabilities with minimal computational overhead, memory usage, or data requirements. Current efforts concentrate on enhancing existing models, such as large language models and object detectors, through techniques like efficient post-hoc methods, novel sampling strategies for data annotation, and optimized training algorithms (including differentially private methods). This research is crucial for making advanced AI techniques more accessible and practical, particularly in resource-constrained environments and applications where privacy is paramount.