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About this paper
Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko
The paper enhances Nearest Neighbor Search (NNS) by learning optimal routing strategies within similarity graphs. It leverages learned representations that capture global graph structure to improve search efficiency and recall, achieving up to 13 percentage points higher Recall@1 compared to traditional methods on standard benchmarks.
Key insights
- Routing decisions in similarity graphs can be significantly improved by incorporating learned representations that capture global graph structure.
- Learning to route allows the search process to overcome the limitations of local minima inherent in greedy, local-information-based navigation.
- An asymmetric neural network architecture effectively balances expressive power for database point representations and computational efficiency for query processing.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.