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David Simcha

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3 papers

2 author rows

NeurIPS Conference 2023 Conference Paper

SOAR: Improved Indexing for Approximate Nearest Neighbor Search

Philip Sun
David Simcha
Dave Dopson
Ruiqi Guo
Sanjiv Kumar

This paper introduces SOAR: S pilling with O rthogonality- A mplified R esiduals, a novel data indexing technique for approximate nearest neighbor (ANN) search. SOAR extends upon previous approaches to ANN search, such as spill trees, that utilize multiple redundant representations while partitioning the data to reduce the probability of missing a nearest neighbor during search. Rather than training and computing these redundant representations independently, however, SOAR uses an orthogonality-amplified residual loss, which optimizes each representation to compensate for cases where other representations perform poorly. This drastically improves the overall index quality, resulting in state-of-the-art ANN benchmark performance while maintaining fast indexing times and low memory consumption.

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ICML Conference 2020 Conference Paper

Accelerating Large-Scale Inference with Anisotropic Vector Quantization

Ruiqi Guo
Philip Sun
Erik Lindgren
Quan Geng
David Simcha
Felix Chern
Sanjiv Kumar

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.

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NeurIPS Conference 2017 Conference Paper

Multiscale Quantization for Fast Similarity Search

Xiang Wu
Ruiqi Guo
Ananda Theertha Suresh
Sanjiv Kumar
Daniel Holtmann-Rice
David Simcha
Felix Yu

We propose a multiscale quantization approach for fast similarity search on large, high-dimensional datasets. The key insight of the approach is that quantization methods, in particular product quantization, perform poorly when there is large variance in the norms of the data points. This is a common scenario for real- world datasets, especially when doing product quantization of residuals obtained from coarse vector quantization. To address this issue, we propose a multiscale formulation where we learn a separate scalar quantizer of the residual norm scales. All parameters are learned jointly in a stochastic gradient descent framework to minimize the overall quantization error. We provide theoretical motivation for the proposed technique and conduct comprehensive experiments on two large-scale public datasets, demonstrating substantial improvements in recall over existing state-of-the-art methods.

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