Imagen de Portada

Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Yun, Vincent-Daniel, Jo, Junhyuk, Lee, Sunwoo
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
por: Yun, Vincent-Daniel, et al.
Publicado: (2026)

Locality-Aware Redundancy Pruning for LLM Depth Compression
por: Yun, Vincent-Daniel, et al.
Publicado: (2026)

Rethinking Layer Redundancy: Calibration Matters More Than Search in LLM Depth Pruning
por: Kim, Minkyu, et al.
Publicado: (2026)

Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
por: Lee, Sunwoo, et al.
Publicado: (2026)

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
por: Lee, Sunwoo, et al.
Publicado: (2025)

Improving MoE Compute Efficiency by Composing Weight and Data Sparsity
por: Kilian, Maciej, et al.
Publicado: (2026)

Asynchronous Sharpness-Aware Minimization For Fast and Accurate Deep Learning
por: Jo, Junhyuk, et al.
Publicado: (2025)

Beyond One-Way Pruning: Bidirectional Pruning-Regrowth for Extreme Accuracy-Sparsity Tradeoff
por: Liu, Junchen, et al.
Publicado: (2025)

Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning
por: Choi, Moonseok, et al.
Publicado: (2023)

Improving Decision Sparsity
por: Sun, Yiyang, et al.
Publicado: (2024)

Shaping Zero-Shot Coordination via State Blocking
por: Kang, Mingu, et al.
Publicado: (2026)

SAFE: Finding Sparse and Flat Minima to Improve Pruning
por: Lee, Dongyeop, et al.
Publicado: (2025)

Catalyst: a Novel Regularizer for Structured Pruning with Auxiliary Extension of Parameter Space
por: Jung, Jaeheun, et al.
Publicado: (2025)

LOTUS: Improving Transformer Efficiency with Sparsity Pruning and Data Lottery Tickets
por: Upadhyay, Ojasw
Publicado: (2024)

Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs
por: Gao, Chang, et al.
Publicado: (2025)

SwiftPrune: Hessian-Free Weight Pruning for Large Language Models
por: Kang, Yuhan, et al.
Publicado: (2025)

LoRA Dropout as a Sparsity Regularizer for Overfitting Control
por: Lin, Yang, et al.
Publicado: (2024)

Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
por: Pang, Jing-Cheng, et al.
Publicado: (2021)

FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization
por: Wang, Haonan, et al.
Publicado: (2025)

Improving Robustness In Sparse Autoencoders via Masked Regularization
por: Narayanaswamy, Vivek, et al.
Publicado: (2026)

Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models
por: Mukherji, Rishav, et al.
Publicado: (2024)

Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogeneous Federated Learning
por: Lim, Jihyun, et al.
Publicado: (2025)

Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity
por: Shrestha, Susav, et al.
Publicado: (2025)

Sparsity and Out-of-Distribution Generalization
por: Aaronson, Scott, et al.
Publicado: (2026)

Synaptic Pruning: A Biological Inspiration for Deep Learning Regularization
por: Vos, Gideon, et al.
Publicado: (2025)

IPPRO: Importance-based Pruning with PRojective Offset for Magnitude-indifferent Structural Pruning
por: Jung, Jaeheun, et al.
Publicado: (2025)

Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
por: Fazlyab, Mahyar, et al.
Publicado: (2023)

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning
por: Wei, Chu-Yuan, et al.
Publicado: (2025)

BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
por: Xu, Peng, et al.
Publicado: (2024)

TopoPrune: Robust Data Pruning via Unified Latent Space Topology
por: Roy, Arjun, et al.
Publicado: (2026)

Improving MLLM Training Efficiency via Stage-Aware Sparsity
por: Shi, Kean, et al.
Publicado: (2025)

Robust Calibration For Improved Weather Prediction Under Distributional Shift
por: Gilda, Sankalp, et al.
Publicado: (2024)

WiSparse: Boosting LLM Inference Efficiency with Weight-Aware Mixed Activation Sparsity
por: Chen, Lei, et al.
Publicado: (2026)

Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
por: Waseda, Futa, et al.
Publicado: (2024)

Deep Learning and Matrix Completion-aided IoT Network Localization in the Outlier Scenarios
por: Kim, Sunwoo
Publicado: (2025)

ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers
por: Patel, Shivam, et al.
Publicado: (2025)

Robust Multi-Agent LLMs under Byzantine Faults
por: Lee, Haejoon, et al.
Publicado: (2026)

SparK: Query-Aware Unstructured Sparsity with Recoverable KV Cache Channel Pruning
por: Liao, Huanxuan, et al.
Publicado: (2025)

The Unseen Frontier: Pushing the Limits of LLM Sparsity with Surrogate-Free ADMM
por: Lee, Kwanhee, et al.
Publicado: (2025)

Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs
por: Ao, Shuang, et al.
Publicado: (2025)