Amortizing Maximum Inner Product Search with Learned Support Functions

Apple researchers have developed a method called amortized Maximum Inner Product Search that uses neural networks to predict the best-matching vector in a database directly. By replacing traditional iterative search algorithms with a regression-based approach, this technique aims to reduce the computational cost of information retrieval tasks common in machine learning.