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Studying the impact of magnitude pruning on contrastive learning methods

Studying the impact of magnitude pruning on contrastive learning methods

Francesco Corti
R. Entezari
Sara Hooker
D. Bacciu
O. Saukh
ArXiv (abs)PDFHTML

Papers citing "Studying the impact of magnitude pruning on contrastive learning methods"

4 / 4 papers shown
Title
3D Point Cloud Network Pruning: When Some Weights Do not Matter
3D Point Cloud Network Pruning: When Some Weights Do not Matter
Amrijit Biswas
M. Hossain
M. M. L. Elahi
A. Cheraghian
Fuad Rahman
Nabeel Mohammed
Shafin Rahman
3DPC
71
2
0
26 Aug 2024
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints
REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints
Francesco Corti
Balz Maag
Joachim Schauer
U. Pferschy
O. Saukh
111
2
0
22 Nov 2023
Unveiling Invariances via Neural Network Pruning
Unveiling Invariances via Neural Network Pruning
Derek Xu
Yizhou Sun
Wei Wang
86
0
0
15 Sep 2023
Relating tSNE and UMAP to Classical Dimensionality Reduction
Relating tSNE and UMAP to Classical Dimensionality Reduction
Andrew Draganov
Simon Dohn
FAtt
107
4
0
20 Jun 2023

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