TY - JOUR
T1 - Results of the Big ANN: NeurIPS'23 competition.
AU - Simhadri, Harsha Vardhan
AU - Aumüller, Martin
AU - Ingber, Amir
AU - Douze, Matthijs
AU - Williams, George
AU - Manohar, Magdalen Dobson
AU - Baranchuk, Dmitry
AU - Liberty, Edo
AU - Liu, Frank
AU - Landrum, Benjamin
AU - Karjikar, Mazin
AU - Dhulipala, Laxman
AU - Chen, Meng
AU - Chen, Yue
AU - Ma, Rui
AU - Zhang, Kai
AU - Cai, Yuzheng
AU - Shi, Jiayang
AU - Chen, Yizhuo
AU - Zheng, Weiguo
AU - Wang, Zihao
AU - Yin, Jie
AU - Huang, Ben
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2024
Y1 - 2024
N2 - 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 [21], 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.
AB - 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 [21], 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.
U2 - 10.48550/arXiv.2409.17424
DO - 10.48550/arXiv.2409.17424
M3 - Journal article
SN - 0000-0000
VL - abs/2409.17424
JO - CoRR
JF - CoRR
ER -