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2206.01816
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-SNE to UMAP with contrastive learning
3 June 2022
Sebastian Damrich
Jan Niklas Böhm
Fred Hamprecht
D. Kobak
SSL
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Papers citing
"From $t$-SNE to UMAP with contrastive learning"
11 / 11 papers shown
Title
Node Embeddings via Neighbor Embeddings
Jan Niklas Böhm
Marius Keute
Alica Guzmán
Sebastian Damrich
Andrew Draganov
D. Kobak
GNN
57
0
0
31 Mar 2025
The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction
Mohammad Tariqul Islam
Jason W. Fleischer
157
0
0
12 Mar 2025
Random Forest Autoencoders for Guided Representation Learning
Adrien Aumon
Shuang Ni
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Jake S. Rhodes
62
0
0
18 Feb 2025
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Ziwei Li
Xiaoqi Wang
Hong-You Chen
Han-Wei Shen
Wei-Lun Chao
FedML
32
0
0
17 Sep 2024
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
Aditya Ravuri
Neil D. Lawrence
28
1
0
27 May 2024
Improving neural network representations using human similarity judgments
Lukas Muttenthaler
Lorenz Linhardt
Jonas Dippel
Robert A. Vandermeulen
Katherine L. Hermann
Andrew Kyle Lampinen
Simon Kornblith
40
29
0
07 Jun 2023
Unsupervised visualization of image datasets using contrastive learning
Jan Boehm
Philipp Berens
D. Kobak
SSL
26
15
0
18 Oct 2022
ParaDime: A Framework for Parametric Dimensionality Reduction
A. Hinterreiter
Christina Humer
Bernhard Kainz
M. Streit
17
5
0
10 Oct 2022
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik
159
301
0
08 Dec 2020
Contrastive Representation Learning: A Framework and Review
Phúc H. Lê Khắc
Graham Healy
A. Smeaton
SSL
AI4TS
175
685
0
10 Oct 2020
Bringing UMAP Closer to the Speed of Light with GPU Acceleration
Corey J. Nolet
V. Lafargue
Edward Raff
Thejaswi Nanditale
Tim Oates
John Zedlewski
Joshua Patterson
29
33
0
01 Aug 2020
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