2 citations · 3 across the 4 of their papers we have counts for
4 papers
Cost-Effective, Low Latency Vector Search with Azure Cosmos DB
Nitish Upreti, Harsha Vardhan Simhadri, Hari Sudan Sundar +33
Vector indexing enables semantic search over diverse corpora and has become an important interface to databases for both users and AI agents. Efficient vector search requires deep…
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…