{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/results-of-the-big-ann-neurips-23-competition","title":"Results of the Big ANN: NeurIPS'23 competition","arxiv_id":"2409.17424","date":"2024-09-25","proceeding":null,"authors":["Harsha Vardhan Simhadri","Martin Aumüller","Amir Ingber","Matthijs Douze","George Williams","Magdalen Dobson Manohar","Dmitry Baranchuk","Edo Liberty","Frank Liu","Ben Landrum","Mazin Karjikar","Laxman Dhulipala","Meng Chen","Yue Chen","Rui Ma","Kai Zhang","Yuzheng Cai","Jiayang Shi","Yizhuo Chen","Weiguo Zheng","Zihao Wan","Jie Yin","Ben Huang"],"abstract":"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. 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