Magnitude-Based Pruning

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Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit
Sep 14, 2026Erdem KoyuncuMagnitude-Based Pruning
One Loop, Two Gains: Can Active Learning win the Lottery for Free?
Sep 9, 2026Benedikt Tscheschner, Eduardo Veas, Marc MasanaActive Learning Magnitude-Based Pruning
Forward-Free LLM Depth Pruning via Weight Redundancy
Sep 9, 2026Vincent-Daniel Yun, Woosang LimMagnitude-Based Pruning Large Language Model Compression
When Does Low-Bit Quantization Preserve the Decisions of Vector Search?
Sep 9, 2026Wenxuan Xiao, Xu CaoLow-Bit Quantization Vector Database
Budget-Aware Compression Pipeline for Single-GPU LLM Inference: Methods, Trade-offs, and Coupling Effects
Aug 30, 2026Hongyu Yu, Yifei ShenLarge Language Model Compression LLM Inference Efficiency
FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees
Aug 12, 2026Zhiqiang Que, Chang Sun, Haiyang Wang +6Low-Bit Quantization Field-Programmable Gate Arrays
InterPruner: Interactive Structured Pruning via Taylor-Implicit Criterion and Language-Prior Modulator for Multimodal Object Detection
Aug 11, 2026Qi Ming, Zihan Yang, Shaoguang Huang +6Magnitude-Based Pruning Modulation
Domain-Aware Pruning: Sparsity and Domain Generalization via Regularized Probabilistic Masking
Aug 9, 2026Parham Sazdar, Mostafa Tavassolipour, Reshad HosseiniMultimodal Domain Generalization Magnitude-Based Pruning
Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models
Aug 8, 2026Ali Janati, Kaoutar El Maghraoui, Xinyi Luo +3Experts Magnitude-Based Pruning
StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning
Aug 5, 2026Timo Bartels, Danish Nazir, Jan Piewek +2Real-Time Semantic Segmentation Magnitude-Based Pruning
ATFlash: Per-RoPE-Wavelength Attention Windows for Compute/Memory-Efficient LLM Inference
Aug 3, 2026Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +1Rotary Position Embedding LLM Inference Efficiency
F-WANDA: Fisher-Reweighted Post-Training Pruning for Sustainable Deployment of Large Language Models
Aug 1, 2026Himanshu MishraLarge Language Model Compression Magnitude-Based Pruning
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Jul 30, 2026Haozhe Hu, Hao Wu, Peiran Yin +3Magnitude-Based Pruning LLM Inference Efficiency
Unified Static-Dynamic Pruning for Efficient LLM Inference
Jul 24, 2026Jinhyeok Kim, Yejoon Lee, Jaeyoung DoLLM Inference Efficiency Magnitude-Based Pruning
An Exact Instrument for State Usage in Selective State-Space Models, and the Input-Driven Migration It Reveals
Jul 13, 2026Raktim BhattacharyaState Space Models Model Selection
Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning
Jul 13, 2026Hao Kong, Di Liu, Xiangzhong Luo +5Magnitude-Based Pruning Convolutional Neural Networks
WaveNet-Style Guitar Amplifier Model Pruning for Real-Time iOS Deployment
Jul 11, 2026Ryota Sato, Eli SilversteinWaveforms Magnitude-Based Pruning
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning
Jul 10, 2026Ivan Ilin, Philip Zmushko, Peter RichtárikParameter-Efficient Fine-Tuning Methods Magnitude-Based Pruning
PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning
Jul 8, 2026Yazdan Jamshidi, Alexey ShvetsMagnitude-Based Pruning Structured Pruning
EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning
Jul 2, 2026Ahin Lee, Sehyun Yun, Taesik GongParameter-Efficient Fine-Tuning Methods Cold Mixture-Of-Expert Models
FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models
Jun 26, 2026Fan Mo, Yuxuan Han, Geng Zhang +2Large Language Model Compression Magnitude-Based Pruning
TMP: Tree-structured Mixed-policy Pruning for Large-scale Image Generation and Editing
Jun 25, 2026Peizhen Zhang, Yang Li, Xunsong Li +10Magnitude-Based Pruning Image Compression
Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT
Jun 25, 2026Jinghan Wang, Yanjun Chen, Wei Zhang +3Magnitude-Based Pruning LLM Inference Efficiency
EMA-FS: Accelerating GBDT Training via Gain-Informed Feature Screening
Jun 24, 2026Yan SongGradient Boosted Tree Feature Selection
Complementary Attention Head Pruning for Efficient Transformers
Jun 17, 2026Yaniv Livertovsky, Shahar Somin, Gonen SingerMagnitude-Based Pruning Hyperparameter
S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices
Jun 16, 2026Marco Deano, Filippo Ziche, Nicola BombieriMagnitude-Based Pruning State Space Models
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
Jun 16, 2026Yifu Ding, Jiacheng Wang, Ge Yang +4Magnitude-Based Pruning Mixed-Precision Quantization
DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction
Jun 13, 2026Fuyan Zhang, Yuqi Li, Qing Xu +2Magnitude-Based Pruning Spatio-Temporal Forecasting
Beyond Layer Importance in Layer-wise Sparsity: An Inter-Layer Perturbation-Absorption Perspective
Jun 13, 2026Tao Jing, Ningxin Wu, Chen Kang +3Magnitude-Based Pruning Sparsity
Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration
Jun 11, 2026Dongyue Wu, Zilin Guo, Xiaoyu Li +4Magnitude-Based Pruning Structured Pruning
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
Jun 10, 2026Romana Qureshi, Hafida Benhidour, Said Kerrache +1Magnitude-Based Pruning Lotteries
Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering
Jun 10, 2026Haoning Xu, Zhaoqing Li, Huimeng Wang +4Large Language Model Compression Magnitude-Based Pruning
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning
Jun 10, 2026Atif Hassan, Swanand Khare, Jiaul H. PaikExtreme Class Imbalance Magnitude-Based Pruning
SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference
Jun 9, 2026Jaeseong Lee, Seung-won Hwang, Samyam RajbhandariMagnitude-Based Pruning Sparsity
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
Jun 8, 2026Haozhe Hu, Hao Wu, Anhao Zhao +4Magnitude-Based Pruning LLM Inference Efficiency
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
Jun 7, 2026Chenhan Jin, Shengze Xu, Qingsong Wang +3Magnitude-Based Pruning Low-Precision Training
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
Jun 5, 2026Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi +1Mixed-Precision Quantization Large Language Model Compression
HARP: Efficient Data Selection for Finetuning Large Language Models
Jun 5, 2026Ning Wang, Zhengxin Zhang, Maosen Tang +3Efficient Data Selection Finetuning
SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs
Jun 3, 2026Yuhao ZhangMixture-Of-Expert Inference Magnitude-Based Pruning
TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts
Jun 3, 2026Jiangyang He, Shaolin Zhu, Deyi XiongLarge Language Model Compression Large Language Model Scale
CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search
Jun 1, 2026Cheonjun ParkMagnitude-Based Pruning Sparsity
Neural Network Compression by Approximate Differential Equivalence
May 31, 2026Ravi Dhiman, Andrea Passarella, Mirco Tribastone +1Convolutional Neural Network Compression Magnitude-Based Pruning
Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits
May 29, 2026Salem Ameen, Sunil VaderaDeep Neural Network Magnitude-Based Pruning
PrunePath: Towards Highly Structured Sparse Language Models
May 27, 2026Zhexuan Gu, Zixun Fu, Yancheng YuanMagnitude-Based Pruning Structured Pruning
Pruning and Distilling Mixture-of-Experts into Dense Language Models
May 27, 2026Junhyuck Kim, Jihun Yun, Haechan Kim +3Moe-Nd Large Language Model Compression
What Linear Probes Miss: Multi-View Probing for Weight-Space Learning
May 22, 2026Eunwoo Heo, Kyeongkook Seo, Jaejun YooWeight-Space Multi-View
Scout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments
May 21, 2026Hoang-Dung Bui, Abhish Khanal, Raihan Islam Arnob +1Multi-Robot Systems Environment
Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding
May 19, 2026Yuhao Shen, Tianyu Liu, Xinyi Hu +9Speculative Decoding Magnitude-Based Pruning
Prune, Update and Trim: Robust Structured Pruning for Large Language Models
May 18, 2026Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidt-ThiemeMagnitude-Based Pruning Feed-Forward
FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing
May 17, 2026Zihan Tang, Leqi Shen, Hui Chen +7Deepseek-Ocr Optical Character Recognition
LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
May 17, 2026Mohammad Mozaffari, Younes Hourri, Mohammad Rastegari +1Magnitude-Based Pruning Sparsity
Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
May 15, 2026Vincent-Daniel Yun, Junhyuk Jo, Sai Praneeth Karimireddy +1Model Activations Magnitude-Based Pruning
On the Stability of Growth in Structural Plasticity
May 14, 2026Lute Lillo, Nick CheneyPlasticity Magnitude-Based Pruning
VIP-COP: Context Optimization for Tabular Foundation Models
May 13, 2026Yilong Chen, Xueying Ding, Leman AkogluTabular Foundation Models In-Context Learning
Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs
May 12, 2026Jingzhou Jiang, Yi Yang, Kar Yan TamMagnitude-Based Pruning Encoding Models
Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments
May 11, 2026Danilo Brajovic, David A. Kreplin, Marco F. HuberMagnitude-Based Pruning Efficient Data Selection
Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning
May 9, 2026Chen Wang, Siyu Hu, Guangming Tan +1Magnitude-Based Pruning Wireless Foundation Models
GETA-3DGS: Automatic Joint Structured Pruning and Quantization for 3D Gaussian Splatting
May 3, 2026Baobing Zhang, Wanxin Sui3D Gaussian Splatting 3D Gaussian
Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning
Apr 29, 2026Weihang Li, Jianchun Liu, Hongli XuMultiple Model Experts Parameter-Efficient Fine-Tuning Methods
A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing
Apr 22, 2026Samaresh Kumar Singh, Joyjit RoyIntelligent Edge Distributed Optimization

Sep 14, 2026 · cs.LGJ/K move · Enter open · S save

Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit

Erdem Koyuncu

We study compute reduction in neural networks through a unified partial versus full computation view, captured by one-shot magnitude pruning in the static regime and early exit in the adaptive regime. In an asymptotic single-neuron model, we prove a concentration theorem for one-shot magnitude pruning with explicit rates. We also introduce the conditional perceptron for early exit and show that its excess generalization error decays as a power of the compute gap, with an exponent that grows to infinity as the alignment between partial and full computations tends to one. We then extend the analysis to deep networks, characterizing how pruning-induced distortions accumulate with depth and deriving a corresponding compute-accuracy tradeoff for frozen-backbone early exit under a neural network Gaussian process model. Numerical simulations corroborate the predicted scaling laws.

Magnitude-Based Pruning

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