Saved in:
| Main Authors: | Zhang, Qiaozhe, Zhang, Ruijie, Sun, Jun, Liu, Yingzhuang |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2306.05857 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Rényi Sharpness: A Novel Sharpness that Strongly Correlates with Generalization
by: Zhang, Qiaozhe, et al.
Published: (2025)
by: Zhang, Qiaozhe, et al.
Published: (2025)
Analog Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective
by: Liao, Limin, et al.
Published: (2025)
by: Liao, Limin, et al.
Published: (2025)
Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
by: Cao, Yang, et al.
Published: (2025)
by: Cao, Yang, et al.
Published: (2025)
How Does Bayes Error Limit Probabilistic Robust Accuracy
by: Zhang, Ruihan, et al.
Published: (2024)
by: Zhang, Ruihan, et al.
Published: (2024)
Information Consistent Pruning: How to Efficiently Search for Sparse Networks?
by: Gharatappeh, Soheil, et al.
Published: (2025)
by: Gharatappeh, Soheil, et al.
Published: (2025)
Digital Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective
by: Liao, Limin, et al.
Published: (2026)
by: Liao, Limin, et al.
Published: (2026)
DSD$^2$: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?
by: Quétu, Victor, et al.
Published: (2023)
by: Quétu, Victor, et al.
Published: (2023)
EvoESAP: Non-Uniform Expert Pruning for Sparse MoE
by: Liu, Zongfang, et al.
Published: (2026)
by: Liu, Zongfang, et al.
Published: (2026)
Towards Fundamental Limits for Active Multi-distribution Learning
by: Zhang, Chicheng, et al.
Published: (2025)
by: Zhang, Chicheng, et al.
Published: (2025)
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
by: Wang, Senmiao, et al.
Published: (2025)
by: Wang, Senmiao, et al.
Published: (2025)
Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured?
by: Liu, Li, et al.
Published: (2024)
by: Liu, Li, et al.
Published: (2024)
The Fundamental Limits of Fraud Detection in Card Payment Networks
by: Dhama, Gaurav
Published: (2026)
by: Dhama, Gaurav
Published: (2026)
SparseSSM: Efficient Selective Structured State Space Models Can Be Pruned in One-Shot
by: Tuo, Kaiwen, et al.
Published: (2025)
by: Tuo, Kaiwen, et al.
Published: (2025)
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations
by: Cheng, Hongrong, et al.
Published: (2023)
by: Cheng, Hongrong, et al.
Published: (2023)
S-STE: Continuous Pruning Function for Efficient 2:4 Sparse Pre-training
by: Hu, Yuezhou, et al.
Published: (2024)
by: Hu, Yuezhou, et al.
Published: (2024)
Diversifying the Expert Knowledge for Task-Agnostic Pruning in Sparse Mixture-of-Experts
by: Zhang, Zeliang, et al.
Published: (2024)
by: Zhang, Zeliang, et al.
Published: (2024)
Max-Affine Spline Insights Into Deep Network Pruning
by: You, Haoran, et al.
Published: (2021)
by: You, Haoran, et al.
Published: (2021)
Growing Winning Subnetworks, Not Pruning Them: A Paradigm for Density Discovery in Sparse Neural Networks
by: Yao, Qihang, et al.
Published: (2025)
by: Yao, Qihang, et al.
Published: (2025)
Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?
by: Zhang, Mingqiao, et al.
Published: (2026)
by: Zhang, Mingqiao, et al.
Published: (2026)
On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy
by: Cui, Jingyi, et al.
Published: (2025)
by: Cui, Jingyi, et al.
Published: (2025)
Approximate Multiplication of Sparse Matrices with Limited Space
by: Wan, Yuanyu, et al.
Published: (2020)
by: Wan, Yuanyu, et al.
Published: (2020)
OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition
by: Zhang, Stephen, et al.
Published: (2024)
by: Zhang, Stephen, et al.
Published: (2024)
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
by: Bair, Anna, et al.
Published: (2023)
by: Bair, Anna, et al.
Published: (2023)
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
by: Loconte, Lorenzo, et al.
Published: (2024)
by: Loconte, Lorenzo, et al.
Published: (2024)
Meta Pruning via Graph Metanetworks : A Universal Meta Learning Framework for Network Pruning
by: Liu, Yewei, et al.
Published: (2025)
by: Liu, Yewei, et al.
Published: (2025)
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
by: Sarmiento, Paulo Yanez, et al.
Published: (2024)
by: Sarmiento, Paulo Yanez, et al.
Published: (2024)
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
by: Tomasini, Umberto, et al.
Published: (2024)
by: Tomasini, Umberto, et al.
Published: (2024)
Concurrent Training and Layer Pruning of Deep Neural Networks
by: Guenter, Valentin Frank Ingmar, et al.
Published: (2024)
by: Guenter, Valentin Frank Ingmar, et al.
Published: (2024)
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
by: Lin, Wu, et al.
Published: (2024)
by: Lin, Wu, et al.
Published: (2024)
What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
by: Zhang, Fan, et al.
Published: (2026)
by: Zhang, Fan, et al.
Published: (2026)
Modality-Aware Zero-Shot Pruning and Sparse Attention for Efficient Multimodal Edge Inference
by: Sui, Yueyuan, et al.
Published: (2026)
by: Sui, Yueyuan, et al.
Published: (2026)
Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
by: Deng, Huiqi, et al.
Published: (2025)
by: Deng, Huiqi, et al.
Published: (2025)
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
by: Wang, Yuhan, et al.
Published: (2026)
by: Wang, Yuhan, et al.
Published: (2026)
How Much Can We Forget about Data Contamination?
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
Artificial Neural Network and Deep Learning: Fundamentals and Theory
by: Hammad, M. M.
Published: (2024)
by: Hammad, M. M.
Published: (2024)
Adaptive Signal Resuscitation: Channel-wise Post-Pruning Repair for Sparse Vision Networks
by: Zhan, Qishi, et al.
Published: (2026)
by: Zhan, Qishi, et al.
Published: (2026)
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
by: Qian, Linglong, et al.
Published: (2024)
by: Qian, Linglong, et al.
Published: (2024)
MISA: Mixture of Indexer Sparse Attention for Long-Context LLM Inference
by: Zhou, Ruijie, et al.
Published: (2026)
by: Zhou, Ruijie, et al.
Published: (2026)
Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
by: Zhang, Qining, et al.
Published: (2023)
by: Zhang, Qining, et al.
Published: (2023)
The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
by: Ishikawa, Takehiro
Published: (2025)
by: Ishikawa, Takehiro
Published: (2025)
Similar Items
-
Rényi Sharpness: A Novel Sharpness that Strongly Correlates with Generalization
by: Zhang, Qiaozhe, et al.
Published: (2025) -
Analog Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective
by: Liao, Limin, et al.
Published: (2025) -
Fundamental Limits of Crystalline Equivariant Graph Neural Networks: A Circuit Complexity Perspective
by: Cao, Yang, et al.
Published: (2025) -
How Does Bayes Error Limit Probabilistic Robust Accuracy
by: Zhang, Ruihan, et al.
Published: (2024) -
Information Consistent Pruning: How to Efficiently Search for Sparse Networks?
by: Gharatappeh, Soheil, et al.
Published: (2025)