1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Individualized non-uniform quantization for vector search
Mariano Tepper, Ted Willke
Embedding vectors are widely used for representing unstructured data and searching through it for semantically similar items. However, the large size of these vectors, due to their…
cs.LG2025
The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
The most popular graph indices for vector search use principles from computational geometry to build the graph. Hence, their formal graph navigability guarantees are only valid in…
cs.CL2024★ 1 cited
Toward Optimal Search and Retrieval for RAG
Alexandria Leto, Cecilia Aguerrebere, Ishwar Bhati +3
Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems f…