Scaling Laws for Grid-Based Approximate Nearest Neighbor Search in High Dimensions

A team of researchers from the University of California, Berkeley published a paper on grid-based approximate nearest neighbor search, finding that grid-based multiprobe algorithms scale better in high dimensions than other methods. This research has implications for applications such as efficient transformer architectures. The paper is available on arXiv and the code is open-sourced on GitHub.