Copertina

MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Zixiao, Zhang, Jingwei, Zhao, Wenqian, Farnia, Farzan, Yu, Bei
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DiffPattern-Flex: Efficient Layout Pattern Generation via Discrete Diffusion
di: Wang, Zixiao, et al.
Pubblicazione: (2025)

ChatPattern: Layout Pattern Customization via Natural Language
di: Wang, Zixiao, et al.
Pubblicazione: (2024)

Sparse Domain Transfer via Elastic Net Regularization
di: Zhang, Jingwei, et al.
Pubblicazione: (2024)

Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
di: Bhuiyan, Samiul Basir, et al.
Pubblicazione: (2025)

Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models
di: Dong, Peijie, et al.
Pubblicazione: (2024)

Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
di: Pan, Rui, et al.
Pubblicazione: (2025)

On the Distributed Evaluation of Generative Models
di: Wang, Zixiao, et al.
Pubblicazione: (2023)

MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
di: Xie, Yanyue, et al.
Pubblicazione: (2024)

An Interpretable Evaluation of Entropy-based Novelty of Generative Models
di: Zhang, Jingwei, et al.
Pubblicazione: (2024)

PIP: Perturbation-based Iterative Pruning for Large Language Models
di: Cao, Yi, et al.
Pubblicazione: (2025)

Fast and Effective Weight Update for Pruned Large Language Models
di: Boža, Vladimír
Pubblicazione: (2024)

CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models
di: Lou, Qian, et al.
Pubblicazione: (2024)

When Kernels Multiply, Clusters Unify: Fusing Embeddings with the Kronecker Product
di: Wu, Youqi, et al.
Pubblicazione: (2025)

Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations
di: Oskouie, Haniyeh Ehsani, et al.
Pubblicazione: (2022)

Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
di: Zhang, Jingwei, et al.
Pubblicazione: (2024)

Keyframe-oriented Vision Token Pruning: Enhancing Efficiency of Large Vision Language Models on Long-Form Video Processing
di: Liu, Yudong, et al.
Pubblicazione: (2025)

FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing
di: Smith, James Seale, et al.
Pubblicazione: (2025)

Efficient Post-Training Pruning of Large Language Models with Statistical Correction
di: Yu, Peiqi, et al.
Pubblicazione: (2026)

Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment
di: Liu, Jun, et al.
Pubblicazione: (2024)

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs
di: Fu, Yao, et al.
Pubblicazione: (2025)

Deterministic Differentiable Structured Pruning for Large Language Models
di: Huang, Weiyu, et al.
Pubblicazione: (2026)

Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
di: Huang, Tiansheng, et al.
Pubblicazione: (2024)

DenoiseRotator: Enhance Pruning Robustness for LLMs via Importance Concentration
di: Gu, Tianteng, et al.
Pubblicazione: (2025)

Gaussian Smoothing in Saliency Maps: The Stability-Fidelity Trade-Off in Neural Network Interpretability
di: Ye, Zhuorui, et al.
Pubblicazione: (2024)

BlockPruner: Fine-grained Pruning for Large Language Models
di: Zhong, Longguang, et al.
Pubblicazione: (2024)

Prompt Perturbation Consistency Learning for Robust Language Models
di: Qiang, Yao, et al.
Pubblicazione: (2024)

Certified Adversarial Robustness via Partition-based Randomized Smoothing
di: Goli, Hossein, et al.
Pubblicazione: (2024)

Large Language Model Pruning
di: Huang, Hanjuan, et al.
Pubblicazione: (2024)

Probing the Robustness of Large Language Models Safety to Latent Perturbations
di: Gu, Tianle, et al.
Pubblicazione: (2025)

PAT: Pruning-Aware Tuning for Large Language Models
di: Liu, Yijiang, et al.
Pubblicazione: (2024)

Sample-aware Adaptive Structured Pruning for Large Language Models
di: Kong, Jun, et al.
Pubblicazione: (2025)

Beware of Calibration Data for Pruning Large Language Models
di: Ji, Yixin, et al.
Pubblicazione: (2024)

EfficientXpert: Efficient Domain Adaptation for Large Language Models via Propagation-Aware Pruning
di: Zhao, Songlin, et al.
Pubblicazione: (2025)

DarwinLM: Evolutionary Structured Pruning of Large Language Models
di: Tang, Shengkun, et al.
Pubblicazione: (2025)

Frustratingly Easy Task-aware Pruning for Large Language Models
di: Tian, Yuanhe, et al.
Pubblicazione: (2025)

Detecting and Pruning Prominent but Detrimental Neurons in Large Language Models
di: Ali, Ameen, et al.
Pubblicazione: (2025)

DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Models
di: Jafari, Donya, et al.
Pubblicazione: (2026)

PermLLM: Learnable Channel Permutation for N:M Sparse Large Language Models
di: Zou, Lancheng, et al.
Pubblicazione: (2025)

FairPair: A Robust Evaluation of Biases in Language Models through Paired Perturbations
di: Dwivedi-Yu, Jane, et al.
Pubblicazione: (2024)

ROSE: Reordered SparseGPT for More Accurate One-Shot Large Language Models Pruning
di: Su, Mingluo, et al.
Pubblicazione: (2026)