Search Results for author:
Found 8 papers, 2 papers with code
SOAR: Improved Indexing for Approximate Nearest Neighbor Search
This paper introduces SOAR: Spilling with Orthogonality-Amplified Residuals, a novel data indexing technique for approximate nearest neighbor (ANN) search.
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets
Hierarchical Agglomerative Clustering (HAC) is one of the oldest but still most widely used clustering methods.
New Loss Functions for Fast Maximum Inner Product Search
In this work, we focus directly on minimizing error in inner product approximation and derive a new class of quantization loss functions.
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
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.
Local Orthogonal Decomposition for Maximum Inner Product Search
More specifically, we decompose a residual vector locally into two orthogonal components and perform uniform quantization and multiscale quantization to each component respectively.
Efficient Inner Product Approximation in Hybrid Spaces
In this paper, we propose a technique that approximates the inner product computation in hybrid vectors, leading to substantial speedup in search while maintaining high accuracy.
Multiscale Quantization for Fast Similarity Search
We propose a multiscale quantization approach for fast similarity search on large, high-dimensional datasets.
Quantization based Fast Inner Product Search
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS).
