2 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…