# learning-based

> Browse 6 items tagged with learning-based.

## Items

- [ELPIS — Graph-Based Similarity Search for Scalable Data Science](https://vectordb.ever.works/es/items/elpis-graph-based-similarity-search-for-scalable-data-science) — VLDB 2023 paper presenting ELPIS, a graph-based similarity search approach that combines graph indexing with learning-based techniques for scalable data science applications on large datasets.
- [Improving ANNS through Learned Adaptive Early Termination](https://vectordb.ever.works/es/items/improving-anns-through-learned-adaptive-early-termination) — SIGMOD 2020 paper proposing learned adaptive early termination for approximate nearest neighbor search, using machine learning to predict when to stop searching, balancing accuracy and latency dynamically.
- [Learning Balanced Tree Indexes for Large-Scale Vector Retrieval](https://vectordb.ever.works/es/items/learning-balanced-tree-indexes-for-large-scale-vector-retrieval) — SIGKDD 2023 paper proposing learned balanced tree indexing for large-scale vector retrieval, using machine learning to construct balanced tree structures optimized for vector similarity search at scale.
- [Learning to Route in Similarity Graphs](https://vectordb.ever.works/es/items/learning-to-route-in-similarity-graphs) — ICML 2019 paper introducing a learned routing approach for similarity graphs, using machine learning to guide greedy search traversal in graph-based approximate nearest neighbor search.
- [LIRA — Learning-based Query-aware Partition Framework for Large-scale ANN Search](https://vectordb.ever.works/es/items/lira-learning-based-query-aware-partition-framework-for-large-scale-ann-search) — WWW 2025 paper proposing LIRA, a learning-based query-aware partition framework designed for large-scale approximate nearest neighbor search, adapting partitions based on query characteristics to improve search efficiency.
- [Routing-Guided Learned Product Quantization for Graph-Based ANNS](https://vectordb.ever.works/es/items/routing-guided-learned-product-quantization-for-graph-based-anns) — ICDE 2024 paper proposing a routing-guided learned product quantization method that enhances graph-based approximate nearest neighbor search by learning optimal quantization guided by graph routing information.

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