Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve
the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the …

Nearest neighbor search is a computational primitive whose efficiency is paramount to many
applications. As such, the literature recently blossomed with many works focusing on …

Academic paper search is an essential task for efficient literature discovery and scientific
advancement. While dense retrieval has advanced various ad-hoc searches, it often …

The maximum inner product search (MIPS), which finds the item with the highest inner
product with a given query user, is an essential problem in the recommendation field …

This work presents an adaptive group testing framework for the range-based high
dimensional near neighbor search problem. Our method efficiently marks each item in a …

High-performance implementations of k-Nearest Neighbor Search (kNN) in low dimensions
use tree-based data structures. Tree algorithms are hard to parallelize on GPUs due to their …

Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal
challenge in the field of machine learning. In recent years, graph-based methods have …

Locality Sensitive Filters are known for offering a quasi-linear space data structure with
rigorous guarantees for the Approximate Near Neighbor search problem. Building on …

We present sDBSCAN, a scalable density-based clustering algorithm in high dimensions
with cosine distance. Utilizing the neighborhood-preserving property of random projections …

This work presents an adaptive group testing framework for the range-based high
dimensional near neighbor search problem. The proposed method detects high-similarity …