Sketch and Lift

Sketch-based methods are emerging as efficient tools for handling large datasets and complex problems across diverse fields. Current research focuses on developing scalable algorithms, such as sketch-and-project methods and implicit neural representations, to improve the speed and accuracy of tasks like clustering, shape retrieval, and planning. These techniques leverage dimensionality reduction and efficient data structures to approximate solutions to computationally expensive problems, offering significant improvements in speed and scalability compared to traditional approaches. The resulting advancements have implications for various applications, including big data analysis, computer vision, and machine learning.