Google Inc.

Philip Wenjie Sun

Sanjiv Kumar

Ruiqi Guo

Example quantization-based approximate nearest neighbors (ANN) search methods and systems (e.g., search engines) are tuned to perform at the speed-recall pareto frontier. With a desired search cost or recall as input, embodiments employ Lagrangian-based methods to perform constrained optimization on theoretically-grounded search cost and recall models. The resulting tunings, when paired with the efficient quantization-based ANN implementation of the embodiments, exhibit excellent performance on standard benchmarks while requiring minimal tuning or configuration complexity.

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New York, NY, United States of America

Philip Wenjie Sun