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researcher

Mark Hildebrand

3 papers hereh-index 5222 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.DB1

identity via Semantic Scholar / OpenAlex

activity
20232025
most citedLocally-Adaptive Quantization for Streaming Vector Search

2 citations · 2 across the 1 of their papers we have counts for

collaborators

3 papers

cs.DB2025

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…

cs.LG2024★ 2 cited

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…

cs.LG2023

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.