arXiv:2607. 01283v1 Announce Type: cross Abstract: Grid-based approaches to approximate nearest neighbor (ANN) search have been absent from modern scaling analyses.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 17582v1 Announce Type: new Abstract: Approximate Nearest Neighbor Search (ANNS) plays a pivotal role in modern deep learning pipelines.
arXiv:2603. 06660v2 Announce Type: replace-cross Abstract: Approximate Nearest Neighbor Search (ANNS) is fundamental to modern AI applications.
Approximate Nearest Neighbor Search (ANNS) plays a pivotal role in modern deep learning pipelines. Recently, many ANNS systems have been proposed to either provide broad functionality or reach high performance.
arXiv:2411. 03253v2 Announce Type: replace-cross Abstract: We propose a general framework for end-to-end learning of data structures.
arXiv:2608. 16270v1 Announce Type: new Abstract: Coreset selection reduces the cost of model training by replacing a large training set with a small representative subset.
arXiv:2608. 15438v1 Announce Type: cross Abstract: Building approximate nearest neighbor (ANN) indexes at billion scale is often dominated by expensive global clustering or graph construction, making time-to-index a first-order systems concern.