Selective Engagement

Selective engagement, in various contexts, focuses on optimizing resource allocation by prioritizing the most relevant information or actions. Current research explores this concept across diverse fields, employing techniques like Bayesian learning, selective low-rank approximation, and active learning to improve efficiency and accuracy in tasks ranging from computer vision and natural language processing to drug discovery and autonomous driving. These advancements offer significant potential for improving the efficiency and performance of machine learning models and for gaining deeper insights into complex systems, such as the human brain.