Practical Vector Search Challenge: NeurIPS 2023 Competition track

This challenge is to encourage the development of indexing data structures and search algorithms for practical variants of the Approximate Nearest Neighbor (ANN) or Vector search problem. These variants are increasingly relevant as vector search becomes commonplace. This challenge has four tracks covering sparse, filtered, out-of-distribution and streaming variants of ANNS. These variants require adapted search algorithms and strategies with different tradeoffs. Participants are encouraged to develop and submit new algorithms that improve on the baselines for these variants. This competition aims at being accessible to participants by limiting the scale of the datasets to about 10 million points. You may request cloud compute credits ($1000) for development by July 31st.