Bibliographic Details
| Title: |
Results of the Big ANN: NeurIPS'23 competition |
| Authors: |
Simhadri, Harsha Vardhan, Aumüller, Martin, Ingber, Amir, Douze, Matthijs, Williams, George, Manohar, Magdalen Dobson, Baranchuk, Dmitry, Liberty, Edo, Liu, Frank, Landrum, Ben, Karjikar, Mazin, Dhulipala, Laxman, Chen, Meng, Chen, Yue, Ma, Rui, Zhang, Kai, Cai, Yuzheng, Shi, Jiayang, Chen, Yizhuo, Zheng, Weiguo, Wan, Zihao, Yin, Jie, Huang, Ben |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Information Retrieval, Computer Science - Data Structures and Algorithms, Computer Science - Machine Learning, Computer Science - Performance, H.3.3 |
| More Details: |
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads. Unlike prior challenges that emphasized scaling up classical ANN search ~\cite{DBLP:conf/nips/SimhadriWADBBCH21}, this competition addressed filtered search, out-of-distribution data, sparse and streaming variants of ANNS. Participants developed and submitted innovative solutions that were evaluated on new standard datasets with constrained computational resources. The results showcased significant improvements in search accuracy and efficiency over industry-standard baselines, with notable contributions from both academic and industrial teams. This paper summarizes the competition tracks, datasets, evaluation metrics, and the innovative approaches of the top-performing submissions, providing insights into the current advancements and future directions in the field of approximate nearest neighbor search.
Comment: Code: https://github.com/harsha-simhadri/big-ann-benchmarks/releases/tag/v0.3.0 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2409.17424 |
| Accession Number: |
edsarx.2409.17424 |
| Database: |
arXiv |
