Based Selection

Based selection encompasses methods for choosing optimal subsets from larger sets of candidates, a crucial task across diverse fields. Current research focuses on improving selection efficiency and accuracy, particularly addressing issues like instability in differentiable architecture search and computational cost in evolutionary algorithms. This involves developing novel criteria beyond simple magnitude-based approaches, incorporating geometric or semantic information, and leveraging machine learning to guide selection processes. These advancements have demonstrably improved performance in applications ranging from neural architecture search and object detection to natural language processing and telepresence robotics.