# Individualized non-uniform quantization for vector search

arXiv: https://arxiv.org/abs/2509.18471 · HTML: https://mlsanity.com/paper/2509.18471/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph](https://mlsanity.com/paper/1707.00143/) (2017)
- [A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2101.12631/) (2021)
- [Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval](https://mlsanity.com/paper/2112.04446/) (2021)
- [A Survey on Locality Sensitive Hashing Algorithms and their Applications](https://mlsanity.com/paper/2102.08942/) (2021)
- [Survey of Vector Database Management Systems](https://mlsanity.com/paper/2310.14021/) (2023)
- [SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2111.08566/) (2021)
- [Similarity search in the blink of an eye with compressed indices](https://mlsanity.com/paper/2304.04759/) (2023)
- [Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD](https://mlsanity.com/paper/1812.09162/) (2018)
- [BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU](https://mlsanity.com/paper/2401.11324/) (2024)
- [Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising](https://mlsanity.com/paper/1607.01869/) (2016)
- [DeDrift: Robust Similarity Search under Content Drift](https://mlsanity.com/paper/2308.02752/) (2023)
- [Locally-Adaptive Quantization for Streaming Vector Search](https://mlsanity.com/paper/2402.02044/) (2024)
- [Low-Precision Quantization for Efficient Nearest Neighbor Search](https://mlsanity.com/paper/2110.08919/) (2021)
- [LeanVec: Searching vectors faster by making them fit](https://mlsanity.com/paper/2312.16335/) (2023)
- [Stabilizing the Kumaraswamy Distribution](https://mlsanity.com/paper/2410.00660/) (2024)

## Abstract (arXiv)

Embedding vectors are widely used for representing unstructured data and searching through it for semantically similar items. However, the large size of these vectors, due to their high-dimensionality, creates problems for modern vector search techniques: retrieving large vectors from memory/storage is expensive and their footprint is costly. In this work, we present NVQ (non-uniform vector quantization), a new vector compression technique that is computationally and spatially efficient in the high-fidelity regime. The core in NVQ is to use novel parsimonious and computationally efficient nonlinearities for building non-uniform vector quantizers. Critically, these quantizers are individually learned for each indexed vector. Our experimental results show that NVQ exhibits improved accuracy compared to the state of the art with a minimal computational cost.