Imagen de Portada

How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhang, Qiaozhe, Zhang, Ruijie, Sun, Jun, Liu, Yingzhuang
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Rényi Sharpness: A Novel Sharpness that Strongly Correlates with Generalization
por: Zhang, Qiaozhe, et al.
Publicado: (2025)

Analog Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective
por: Liao, Limin, et al.
Publicado: (2025)

Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
por: Cao, Yang, et al.
Publicado: (2025)

How Does Bayes Error Limit Probabilistic Robust Accuracy
por: Zhang, Ruihan, et al.
Publicado: (2024)

Information Consistent Pruning: How to Efficiently Search for Sparse Networks?
por: Gharatappeh, Soheil, et al.
Publicado: (2025)

DSD$^2$: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?
por: Quétu, Victor, et al.
Publicado: (2023)

EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
por: Liu, Zongfang, et al.
Publicado: (2026)

Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured?
por: Liu, Li, et al.
Publicado: (2024)

Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
por: Wang, Senmiao, et al.
Publicado: (2025)

Towards Fundamental Limits for Active Multi-distribution Learning
por: Zhang, Chicheng, et al.
Publicado: (2025)

SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot
por: Tuo, Kaiwen, et al.
Publicado: (2025)

Digital Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective
por: Liao, Limin, et al.
Publicado: (2026)

The Fundamental Limits of Fraud Detection in Card Payment Networks
por: Dhama, Gaurav
Publicado: (2026)

S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training
por: Hu, Yuezhou, et al.
Publicado: (2024)

Growing Winning Subnetworks, Not Pruning Them: A Paradigm for Density Discovery in Sparse Neural Networks
por: Yao, Qihang, et al.
Publicado: (2025)

Max-Affine Spline Insights Into Deep Network Pruning
por: You, Haoran, et al.
Publicado: (2021)

A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
por: Cheng, Hongrong, et al.
Publicado: (2023)

Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
por: Zhang, Zeliang, et al.
Publicado: (2024)

On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy
por: Cui, Jingyi, et al.
Publicado: (2025)

Approximate Multiplication of Sparse Matrices with Limited Space
por: Wan, Yuanyu, et al.
Publicado: (2020)

Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?
por: Zhang, Mingqiao, et al.
Publicado: (2026)

Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
por: Sarmiento, Paulo Yanez, et al.
Publicado: (2024)

What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
por: Loconte, Lorenzo, et al.
Publicado: (2024)

OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition
por: Zhang, Stephen, et al.
Publicado: (2024)

Concurrent Training and Layer Pruning of Deep Neural Networks
por: Guenter, Valentin Frank Ingmar, et al.
Publicado: (2024)

Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference
por: Sui, Yueyuan, et al.
Publicado: (2026)

Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
por: Bair, Anna, et al.
Publicado: (2023)

How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
por: Tomasini, Umberto, et al.
Publicado: (2024)

Adaptive Signal Resuscitation: Channel-wise Post-Pruning Repair for Sparse Vision Networks
por: Zhan, Qishi, et al.
Publicado: (2026)

Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
por: Lin, Wu, et al.
Publicado: (2024)

What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
por: Zhang, Fan, et al.
Publicado: (2026)

Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
por: Deng, Huiqi, et al.
Publicado: (2025)

Artificial Neural Network and Deep Learning: Fundamentals and Theory
por: Hammad, M. M.
Publicado: (2024)

Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
por: Liu, Yewei, et al.
Publicado: (2025)

Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
por: Zhang, Qining, et al.
Publicado: (2023)

How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
por: Qian, Linglong, et al.
Publicado: (2024)

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
por: Wang, Yuhan, et al.
Publicado: (2026)

Pruning for Sparse Diffusion Models based on Gradient Flow
por: Wan, Ben, et al.
Publicado: (2025)

Pruning is Optimal for Learning Sparse Features in High-Dimensions
por: Vural, Nuri Mert, et al.
Publicado: (2024)

Fundamental Limits of Neural Network Sparsification: Evidence from Catastrophic Interpretability Collapse
por: Roy, Dip, et al.
Publicado: (2026)