AWS Machine Learning Blog

NOVEMBER 18, 2024

Amazon OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service, a fully managed service that makes it simple to perform interactive log analytics, real-time application monitoring, website search, and vector search with its k-nearest neighbor (kNN) plugin.

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K-nearest Neighbors AWS ML ML 121

AWS Machine Learning Blog

NOVEMBER 9, 2023

Formally, often k-nearest neighbors (KNN) or approximate nearest neighbor (ANN) search is often used to find other snippets with similar semantics. In these two studies, commissioned by AWS, developers were asked to create a medical software application in Java that required use of their internal libraries.

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AWS Natural Language Processing K-nearest Neighbors Computer Science 119

AWS Machine Learning Blog

JUNE 9, 2023

For example, Amazon GuardDuty , the native AWS threat detection service, uses a graph with billions of edges to improve the coverage and accuracy of its threat intelligence. To solve the problem of finding the field of study for any given paper, simply perform a k-nearest neighbor search on the embeddings.

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ML ML K-nearest Neighbors Machine Learning 110

AWS Machine Learning Blog

FEBRUARY 10, 2023

We design a K-Nearest Neighbors (KNN) classifier to automatically identify these plays and send them for expert review. Prior to AWS, he obtained his MCS from West Virginia University and worked as computer vision researcher at Midea. These plays could potentially be mislabeled and deserve manual inspection.

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ML ML Machine Learning Machine Learning 91

Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors 52

K-nearest Neighbors Clustering AI AI 52

Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors 52

K-nearest Neighbors Clustering AI AI 52