Network Pruning
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Benchmarks
Most implemented
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Pruning Filters for Efficient ConvNets
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
SNIP: Single-shot Network Pruning based on Connection Sensitivity
A Simple and Effective Pruning Approach for Large Language Models
Papers
CutClean: Neural Network Pruning for Privacy-Preserving Inference
Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns. We show that this privacy leakage can occur even in the absence of representation imbalances that lead to tradi…
Network PruningFinding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance. Although the Lottery Ticket Hypothesis (LTH) shows that sparse subnetworks can match dense …
Network PruningRelative Repairability: A Calibration-Based Diagnostic for High-Sparsity Post-Pruning Allocation
At very high sparsity, neural network pruning does more than decide which weights remain. It also determines where pruning induced damage is placed across the network, and whether that damage can be recovered by a fixed …
Network PruningSelection Plateau and a Sparsity-Dependent Hierarchy of Pruning Features
We identify a Selection Plateau phenomenon in one-shot neural network pruning: all rank-monotone weight scorers converge to identical accuracy at fixed sparsity, independent of functional form. We propose the Sparsity-In…
Network PruningGraph Normalization: Fast Binarizing Dynamics for Differentiable MWIS
We introduce Graph Normalization (GN), a principled dynamical system on graphs that serves as a differentiable approximation engine for the NP-hard Maximum Weight Independent Set (MWIS) problem. MWIS encompasses many com…
Network PruningSubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transport
Federated Learning (FL) enables collaborative model training while preserving data privacy, but its practical deployment is hampered by system and statistical heterogeneity. While federated network pruning offers a path …
Personalized Federated LearningNetwork Pruning