5 papers
ASH: Asymmetric Scalar Hashing With Learned Dimensionality Reduction for High-Fidelity Vector Quantization
Mariano Tepper, Theodore Willke
For a long time, additive quantizers, such as product quantization, have been considered the gold standard in terms of accuracy and efficiency. Recently, scalar quantization has re…
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…
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…
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…