Routing-Guided Learned Product Quantization for Graph-Based ANNS

VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.

SIGMOD 2020 paper proposing learned adaptive early termination for approximate nearest neighbor search, using machine learning to predict when to stop searching, balancing accuracy and latency dynamically.

ICML 2019 paper introducing a learned routing approach for similarity graphs, using machine learning to guide greedy search traversal in graph-based approximate nearest neighbor search.

A commutative vector quantization method for KV cache compression that reduces FP16 cache size by 87.5% with 2-bit quantization and enables 1-bit quantization, allowing LLaMA-3.1 8B to run with 128K context on a single RTX 4090 GPU.

VLDB 2025 paper proposing efficient level navigation with shortcuts for accelerating approximate nearest neighbor search in hierarchical graph indexes, improving traversal speed across multi-layer graph structures.

SIGKDD 2025 paper proposing adaptive awareness capabilities for graph-based approximate nearest neighbor search, enabling the search algorithm to dynamically adjust its strategy based on local graph characteristics and query properties.