Automating Nearest Neighbor Search Configuration with Constrained Optimization

Understand

The approximate nearest neighbor (ANN) search problem is fundamental to efficiently serving many real-world machine learning applications.

• A number of techniques have been developed for ANN search that are efficient, accurate, and scalable.
• However, such techniques typically have a number of parameters that affect the speed-recall tradeoff, and exhibit poor performance when such parameters aren't properly set.
• Tuning these parameters has traditionally been a manual process, demanding in-depth knowledge of the underlying search algorithm.