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Measuring What Matters: Intrinsic Distance Preservation as a Robust Metric for Embedding Quality

Measuring What Matters: Intrinsic Distance Preservation as a Robust Metric for Embedding Quality

Steven N. Hart
R. Maulik
ArXiv (abs)PDFHTML

Papers citing "Measuring What Matters: Intrinsic Distance Preservation as a Robust Metric for Embedding Quality"

4 / 4 papers shown
Title
The Faiss library
The Faiss library
Matthijs Douze
Alexandr Guzhva
Chengqi Deng
Jeff Johnson
Gergely Szilvasy
Pierre-Emmanuel Mazaré
Maria Lomeli
Lucas Hosseini
Hervé Jégou
199
184
0
16 Jan 2024
Unsupervised Embedding Quality Evaluation
Unsupervised Embedding Quality Evaluation
Anton Tsitsulin
Marina Munkhoeva
Bryan Perozzi
SSL
61
7
0
26 May 2023
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
402
18,913
0
13 Feb 2020
DeepWalk: Online Learning of Social Representations
DeepWalk: Online Learning of Social Representations
Bryan Perozzi
Rami Al-Rfou
Steven Skiena
HAI
277
9,816
0
26 Mar 2014

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