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Localizing Task Information for Improved Model Merging and Compression Ke Wang * 1 Nikolaos Dimitriadis * 1 Guillermo Ortiz-Jim´enez 2 3 Franc¸ois Fleuret 4 Pascal Frossard 1 Abstract Model merging and task arithmetic have emerged as promising scalable approaches to merge multiple single-task checkpoints to one multi-task
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application/pdf
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1328579 bytes
Uploaded On:
2024-06-16
Abstract:
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1
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Parsed
Author:
Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jiménez, François Fleuret, Pascal Frossard
CreationDate:
2024-05-14T01:16:55+00:00
Creator:
LaTeX with hyperref
Keywords:
Machine Learning, ICML
ModDate:
2024-05-14T01:16:55+00:00
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This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) kpathsea version 6.3.5
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pdfTeX-1.40.25
Subject:
Proceedings of the International Conference on Machine Learning 2024
Title:
Localizing Task Information for Improved Model Merging and Compression
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False
Pages:
20
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