2 citations · 3 across the 2 of their papers we have counts for
4 papers
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…
GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction
Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere +1
Embedding models can generate high-dimensional vectors whose similarity reflects semantic affinities. Thus, accurately and timely retrieving those vectors in a large collection tha…
Locally-Adaptive Quantization for Streaming Vector Search
Cecilia Aguerrebere, Mark Hildebrand, Ishwar Singh Bhati +2
Retrieving the most similar vector embeddings to a given query among a massive collection of vectors has long been a key component of countless real-world applications. The recentl…
LeanVec: Searching vectors faster by making them fit
Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere +2
Modern deep learning models have the ability to generate high-dimensional vectors whose similarity reflects semantic resemblance. Thus, similarity search, i.e., the operation of re…