Retrieval of relevant vectors produced by representation learning can critically influence the efficiency in Natural Language Processing (NLP) tasks. Presented herein are systems and methods for searching vectors via a typical nonmetric matching function: inner product. Embodiments, which construct an approximate Inner Product Delaunay Graph (IPDG) for top-1 Maximum Inner Product Search (MIPS), transform retrieving the most suitable latent vectors into a graph search problem with great benefits of efficiency. Experiments on data representations learned for different machine learning tasks verify the outperforming effectiveness and efficiency of IPDG embodiments.
EFFICIENT RETRIEVAL OF TOP SIMILARITY REPRESENTATIONS