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Poster
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo · Philip Sun · Erik Lindgren · Quan Geng · David Simcha · Felix Chern · Sanjiv Kumar

Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 04:00 PM -- 04:45 PM (PDT) @ Virtual
in Poster Session 21 »

Quantization based techniques are the current state-of-the-art for scaling maximum inner product search to massive databases. Traditional approaches to quantization aim to minimize the reconstruction error of the database points. Based on the observation that for a given query, the database points that have the largest inner products are more relevant, we develop a family of anisotropic quantization loss functions. Under natural statistical assumptions, we show that quantization with these loss functions leads to a new variant of vector quantization that more greatly penalizes the parallel component of a datapoint's residual relative to its orthogonal component. The proposed approach, whose implementation is open-source, achieves state-of-the-art results on the public benchmarks available at ann-benchmarks.com.

Keywords: Information Retrieval · Large Scale Learning and Big Data · Recommender Systems · Applications - Other
[ Slides] 

Author Information

Ruiqi Guo (Google Research)
Philip Sun (Google)
Erik Lindgren (Google Research)
Quan Geng (Google)
David Simcha (Google)
Felix Chern (Google AI)
Sanjiv Kumar (Google Research, NY)

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