12 citations · 18 across the 13 of their papers we have counts for
5 papers · 1 filter
Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
Vihan Lakshman, Blaise Munyampirwa, Julian Shun +1
Modern vector databases enable efficient retrieval over high-dimensional neural embeddings, powering applications from web search to retrieval-augmented generation. However, classi…
CAPS: A Practical Partition Index for Filtered Similarity Search
Gaurav Gupta, Jonah Yi, Benjamin Coleman +3
With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of c…
Low-Precision Quantization for Efficient Nearest Neighbor Search
Anthony Ko, Iman Keivanloo, Vihan Lakshman +1
Fast k-Nearest Neighbor search over real-valued vector spaces (KNN) is an important algorithmic task for information retrieval and recommendation systems. We present a method for u…
Embracing Structure in Data for Billion-Scale Semantic Product Search
Vihan Lakshman, Choon Hui Teo, Xiaowen Chu +4
We present principled approaches to train and deploy dyadic neural embedding models at the billion scale, focusing our investigation on the application of semantic product search.…
Semantic Product Search
Priyanka Nigam, Yiwei Song, Vijai Mohan +7
We study the problem of semantic matching in product search, that is, given a customer query, retrieve all semantically related products from the catalog. Pure lexical matching via…