1 citations · 3 across the 4 of their papers we have counts for
4 papers
CleANN: Efficient Full Dynamism in Graph-based Approximate Nearest Neighbor Search
Ziyu Zhang, Yuanhao Wei, Joshua Engels +1
Approximate nearest neighbor search (ANNS) has become a quintessential algorithmic problem for various other foundational data tasks for AI workloads. Graph-based ANNS indexes have…
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
Nicholas Meisburger, Vihan Lakshman, Benito Geordie +9
Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities. Present…
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries
Joshua Engels, Benjamin Coleman, Vihan Lakshman +1
We study the problem of with . This task is analogous to traditional near-neighbor search, with the exception that both th…
Practical Near Neighbor Search via Group Testing
Joshua Engels, Benjamin Coleman, Anshumali Shrivastava
We present a new algorithm for the approximate near neighbor problem that combines classical ideas from group testing with locality-sensitive hashing (LSH). We reduce the near neig…