Papers with Code 아카이브
Network Pruning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 5
- 보존된 평가 기록
- 34
Network Pruning is a popular approach to reduce a heavy network to obtain a light-weight form by removing redundancy in the heavy network. In this approach, a complex over-parameterized network is first trained, then pruned based on come criterions, and finally fine-tuned to achieve comparable performance with reduced parameters.
Source: Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge
Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks
Guiding Evolutionary AutoEncoder Training with Activation-Based Pruning Operators
ReplaceMe: Network Simplification via Layer Pruning and Linear Transformations
Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate
Hyperflows: Pruning Reveals the Importance of Weights
Boosting Large Language Models with Mask Fine-Tuning
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks
Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs
Finding Stable Subnetworks at Initialization with Dataset Distillation
Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems
NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches
Signal Collapse in One-Shot Pruning: When Sparse Models Fail to Distinguish Neural Representations
An Efficient Row-Based Sparse Fine-Tuning
Automatic Pruning via Structured Lasso with Class-wise Information
Exploring Neural Network Pruning with Screening Methods
B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning
Compact Bayesian Neural Networks via pruned MCMC sampling
Neural Architecture Codesign for Fast Physics Applications
Scalable iterative pruning of large language and vision models using block coordinate descent
Adapting the Biological SSVEP Response to Artificial Neural Networks
Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training
Mutual Information Preserving Neural Network Pruning
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models
Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
Aggressive Post-Training Compression on Extremely Large Language Models
Investigating the Effect of Network Pruning on Performance and Interpretability
MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.