arXiv AI
Hypergraph Embedding Indexing for Efficient Dense Vector Retrieval
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jul 7
Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls
arXiv:2607. 03515v1 Announce Type: cross Abstract: In many machine learning applications, the most relevant items for a query should be efficiently retrieved.
arXiv AI
Aug 18
Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement
arXiv:2608. 16628v1 Announce Type: new Abstract: Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple graphs, which fails to capture the intricate, high-order correlations among heterogeneous entities, such as the N-ary relationships between a visual chart, its scattered textual descriptions, and underlying numerical data.
arXiv Machine Learning
Jul 14
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection
arXiv:2605. 01965v2 Announce Type: replace Abstract: A classical vector retrieval problem typically considers a \emph{single} query embedding vector as input and retrieves the most similar embedding vectors from a vector database.
arXiv AI
Jun 17
Non-negative Elastic Net Decoding for Information Retrieval
arXiv:2606. 17910v1 Announce Type: cross Abstract: Dense retrieval has become the dominant paradigm in information retrieval, in which each document is scored against a query by the inner product of their vector embeddings, and the top-$k$ documents by score are retrieved for this query.
arXiv Machine Learning
Aug 7
Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings
arXiv:2608. 05724v1 Announce Type: cross Abstract: Sparse word embedding pipelines can avoid dense co-occurrence matrix materialization, dense factorization, and gradient training while still relying on sparse global corpus statistics.
arXiv Machine Learning
Aug 5
VIBE: Vector Index Benchmark for Embeddings
arXiv:2505. 17810v2 Announce Type: replace Abstract: Approximate nearest neighbor (ANN) search is a performance-critical component of many machine learning pipelines, and rigorous benchmarking is essential for assessing the performance of vector indexes for ANN search.