Github Vectordb Ntu Extended Rabitq Sigmod 2025 Practical And

Github Vectordb Ntu Extended Rabitq Sigmod 2025 Practical And [sigmod 2025] practical and asymptotically optimal quantization of high dimensional vectors in euclidean space for approximate nearest neighbor search. replace your scalar and binary quantization with rabitq seamlessly. enjoy blazingly fast distance computation with dominant accuracy. Extended rabitq for allowing more flexible quantization with varying compression rates (sigmod'25) symphonyqg for combining graph based index with quantization (sigmod'25).

Exrabitq Has Been Accepted By Sigmod 25 Vector Database Group Ntu [sigmod 2025] practical and asymptotically optimal quantization of high dimensional vectors in euclidean space for approximate nearest neighbor search. replace your scalar and binary quantization with rabitq seamlessly. enjoy blazingly fast distance computation with dominant accuracy. The project proposes a novel quantization algorithm developped from rabitq. the algorithm supports to compress high dimensional vectors with arbitrary compression rates. its computation is exactly the same as the classical scalar quantization and has dominant accuracy under same compression rates. Among these methods, a recent algorithm called rabitq achieves the state of the art performance and provides an asymptotically optimal theoretical error bound. rabitq uses 1 bit per dimension for quantization and compresses vectors with a large compression rate. Sigmod'24 rabitq: quantizing high dimensional vectors with a theoretical error bound for approximate nearest neighbor search jianyang gao and cheng long. in acm sigmod international conference on management of data, 2024.

Rabitq Sigmod 2024 知乎 Among these methods, a recent algorithm called rabitq achieves the state of the art performance and provides an asymptotically optimal theoretical error bound. rabitq uses 1 bit per dimension for quantization and compresses vectors with a large compression rate. Sigmod'24 rabitq: quantizing high dimensional vectors with a theoretical error bound for approximate nearest neighbor search jianyang gao and cheng long. in acm sigmod international conference on management of data, 2024. The rabitq library provides efficient and lightweight implementations of the rabitq quantization algorithm (1 bit version and multi bit version) and its applications in high dimensional vector search. In this paper, we introduce a new quantization method to address this limitation by extending rabitq. the new method inherits the theoretical guarantees of rabitq and achieves the asymptotic optimality in terms of the trade off between space and error bounds as to be proven in this study. We propose a new quantization method by extending rabitq. the method constructs the codebook via shifting, normaliz ing and randomly rotating vectors of bit unsigned integers. based on the design, it inherits rabitq’s unbiased estima tor for distance estimation. In this post, we will introduce our extended version of rabitq, which presents a new strategy for minimizing the error when more bits are used. this allows the rabitq method to support the quantization of an arbitrary compression rate.