2 citations · 5 across the 6 of their papers we have counts for
6 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…
Similarity search in the blink of an eye with compressed indices
Cecilia Aguerrebere, Ishwar Bhati, Mark Hildebrand +2
Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity sear…
MARS: Memory Aware Reordered Source
Ishwar Bhati, Udit Dhawan, Jayesh Gaur +2
Memory bandwidth is critical in today's high performance computing systems. The bandwidth is particularly paramount for GPU workloads such as 3D Gaming, Imaging and Perceptual Comp…