Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2402.07839
Cited By
v1
v2 (latest)
Towards Meta-Pruning via Optimal Transport
12 February 2024
Alexander Theus
Olin Geimer
Friedrich Wicke
Thomas Hofmann
Sotiris Anagnostidis
Sidak Pal Singh
MoMe
Re-assign community
ArXiv (abs)
PDF
HTML
Github (16★)
Papers citing
"Towards Meta-Pruning via Optimal Transport"
6 / 6 papers shown
MergeMoE: Efficient Compression of MoE Models via Expert Output Merging
Ruijie Miao
Yilun Yao
Zihan Wang
Z. Wang
Bairen Yi
LingJun Liu
Yikai Zhao
Tong Yang
MoMe
164
1
0
16 Oct 2025
Smooth Model Compression without Fine-Tuning
Christina Runkel
Natacha Kuete Meli
Jovita Lukasik
A. Biguri
Carola-Bibiane Schönlieb
Michael Moeller
245
0
0
30 May 2025
Forget the Data and Fine-Tuning! Just Fold the Network to Compress
International Conference on Learning Representations (ICLR), 2025
Dong Wang
Haris Šikić
Lothar Thiele
O. Saukh
331
1
0
14 Feb 2025
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance
Mostafa Hussien
Mahmoud Afifi
K. Nguyen
M. Cheriet
243
2
0
21 Oct 2024
Subspace Node Pruning
Joshua Offergeld
Marcel van Gerven
Nasir Ahmad
353
0
0
26 May 2024
PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs
Max Zimmer
Megi Andoni
Christoph Spiegel
Sebastian Pokutta
VLM
513
15
0
23 Dec 2023
1
