50 citations · 61 across the 6 of their papers we have counts for
7 papers
Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality
Filipe Miguel Gonçalves de Almeida, CJ Carey, Hendrik Fichtenberger +8
Learning and constructing large-scale graphs has attracted attention in recent decades, resulting in a rich literature that introduced various systems, tools, and algorithms. Grale…
SOAR: Improved Indexing for Approximate Nearest Neighbor Search
Philip Sun, David Simcha, Dave Dopson +2
This paper introduces SOAR: Spilling with Orthogonality-Amplified Residuals, a novel data indexing technique for approximate nearest neighbor (ANN) search. SOAR extends upon previo…
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets
Baris Sumengen, Anand Rajagopalan, Gui Citovsky +6
Hierarchical Agglomerative Clustering (HAC) is one of the oldest but still most widely used clustering methods. However, HAC is notoriously hard to scale to large data sets as the…
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Ruiqi Guo, Philip Sun, Erik Lindgren +4
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…
Local Orthogonal Decomposition for Maximum Inner Product Search
Xiang Wu, Ruiqi Guo, Sanjiv Kumar +1
Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In s…
Efficient Inner Product Approximation in Hybrid Spaces
Xiang Wu, Ruiqi Guo, David Simcha +2
Many emerging use cases of data mining and machine learning operate on large datasets with data from heterogeneous sources, specifically with both sparse and dense components. For…
Quantization based Fast Inner Product Search
Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski +1
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, l…