Research paper by Chen et al. examining the meaningfulness of nearest neighbor search in high-dimensional spaces. Analyzes limitations and implications for vector similarity search. Key for understanding ANN effectiveness.

FusionANNS architecture by Bing Tian et al. for billion-scale ANN search using CPU/GPU cooperation.

Paper by Tepper et al. proposing GleanVec, a method to accelerate vector search using minimalist nonlinear dimensionality reduction. Improves efficiency for high-dimensional vector queries.

Highly-efficient billion-scale approximate nearest neighbor search algorithm introduced by Chen et al. Focuses on scalability and performance for large datasets in high-dimensional spaces. Relevant for vector database indexing techniques.

Framework by Wei et al. for high-dimensional ANN search using subspace collision techniques. Offers efficiency and accuracy improvements for vector databases.

Explores and benchmarks distance comparison operators for ANN. arXiv preprint arXiv:2403.13491 (2024) by Zeyu Wang et al. Aids in vector search optimization.