14 citations · 29 across the 4 of their papers we have counts for
4 papers · 1 filter
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
Harsha Vardhan Simhadri, George Williams, Martin Aumüller +9
Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets, typically of 1 million poi…
Distilling the Knowledge from Conditional Normalizing Flows
Dmitry Baranchuk, Vladimir Aliev, Artem Babenko
Normalizing flows are a powerful class of generative models demonstrating strong performance in several speech and vision problems. In contrast to other generative models, normaliz…
Towards Similarity Graphs Constructed by Deep Reinforcement Learning
Dmitry Baranchuk, Artem Babenko
Similarity graphs are an active research direction for the nearest neighbor search (NNS) problem. New algorithms for similarity graph construction are continuously being proposed a…
Learning to Route in Similarity Graphs
Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin +1
Recently similarity graphs became the leading paradigm for efficient nearest neighbor search, outperforming traditional tree-based and LSH-based methods. Similarity graphs perform…