Dynamic Sampling

Dynamic sampling optimizes data acquisition and processing by selectively focusing on the most informative data points, rather than uniformly sampling the entire dataset. Current research explores this concept across diverse fields, employing techniques like Langevin dynamics, proximal frameworks for log-concave distributions, and neural network-based approaches for efficient score estimation and adaptive sampling schedules. This methodology promises significant improvements in computational efficiency and accuracy for applications ranging from large language model training and theorem proving to mass spectrometry imaging and robotic control, ultimately accelerating scientific discovery and technological advancement.