modelfocusing
Modelfocusing is a broad term used in machine learning to describe strategies that tailor the computational effort of a model to the parts of the input or task that are most informative. The goal is to concentrate resources where they have the greatest impact, enabling faster inference, lower energy use, and often improved predictive performance, particularly in environments with limited compute or noisy data.
Techniques associated with modelfocusing include attention mechanisms that weigh tokens, regions, or features to highlight relevance;
Applications span natural language processing, computer vision, speech recognition, time-series analysis, and on-device inference for mobile
Challenges include overhead from focusing decisions, potential instability during training, and the risk that the mechanism