Machine learning and high dimensional vector search
Not on the map. Why?
Abstract (arXiv)
Machine learning and vector search are two research topics that developed in parallel in nearby communities. However, unlike many other fields related to big data, machine learning has not significantly impacted vector search. In this opinion paper we attempt to explain this oddity. Along the way, we wander over the numerous bridges between the two fields.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 36 of its 49 references that are in the map. First posted on arXiv 24 Feb 2025.
Builds on
36 of 49 references.
References · 36
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
- 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
- 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
- 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
- 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,210
- 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
- 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
24 more
- 2020A Survey on Contrastive Self-supervised Learningcited by 554
- 2024The Faiss librarycited by 376
- 2019Deep Learning Recommendation Model for Personalization and Recommendation Systemscited by 327
- 2024Extreme Compression of Large Language Models via Additive Quantizationcited by 232
- 2017The Case for Learned Index Structurescited by 188
- 2022A Self-Supervised Descriptor for Image Copy Detectioncited by 176
- 2018ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithmscited by 130
- 2023Unlimiformer: Long-Range Transformers with Unlimited Length Inputcited by 128
- 2018Spreading vectors for similarity searchcited by 118
- 2024RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrievalcited by 116
- 2024MagicPIG: LSH Sampling for Efficient LLM Generationcited by 111
- 2019MultiGrain: a unified image embedding for classes and instancescited by 85
- 2019And the Bit Goes Down: Revisiting the Quantization of Neural Networkscited by 78
- 2024RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Searchcited by 48
- 2022Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Searchcited by 45
- 2024Residual Quantization with Implicit Neural Codebookscited by 25
- 2024Results of the Big ANN: NeurIPS'23 competitioncited by 22
- 2022TPU-KNN: K Nearest Neighbor Search at Peak FLOP/scited by 18
- 2019Unsupervised Neural Quantization for Compressed-Domain Similarity Searchcited by 15
- 2024RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Searchcited by 12
- 2025Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Artcited by 11
- 2025Inference-time sparse attention with asymmetric indexingcited by 11
- 2025Qinco2: Vector Compression and Search with Improved Implicit Neural Codebookscited by 8
- 2021Nearest neighbor search with compact codes: A decoder perspectivecited by 5
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Citing papers per year: 2026: 1.
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