ELO-Mask: Effective and Layerwise Optimization of Mask for Sparse LLMs
To address the issue of the substantial computational resource consumption during the inference phase of large language models due to their vast number of parameters, model sparsification is an effective solution. However, current sparsification methods for large models are costly. We propose a comp...
Saved in:
| Main Authors: | Bingjie Xiang, Jiarui Wu, Xiaoying Han, Qian Gu, Fei Chao, Xiao Yang, Fan Wu, Xin Fu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10753603/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Blind mask template attacks on masked cryptographic algorithm
by: Yi WANG, et al.
Published: (2019-01-01) -
Mask Material Filtration Efficiency and Mask Fitting at the Crossroads: Implications during Pandemic Times
by: Karin Ardon-Dryer, et al.
Published: (2021-03-01) -
Extending Randomness-Free First-Order Masking Schemes and Applications to Masking-Friendly S-boxes
by: Lixuan Wu, et al.
Published: (2024-12-01) -
SparseBatch: Communication-efficient Federated Learning with Partially Homomorphic Encryption
by: Chong Wang, et al.
Published: (2025-01-01) -
Cross-Temporal Self-Supervised Learning With Superpixel Mask for Multitemporal Land Cover Classification
by: Qingwang Wang, et al.
Published: (2025-01-01)