Pruning

AAAI 2025arXiv:2409.10197

#2

Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Weihao Ye, Qiong Wu, Wenhao Lin et al.

visual token pruningmultimodal large language modelsattention distribution divergencetraining-free pruning+3

64

ICML 2024arXiv:2406.02924

#3

Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models

Peijie Dong, Lujun Li, Zhenheng Tang et al.

symbolic pruning metricspost-training pruninglarge language modelsgenetic programming+2

54

AAAI 2024arXiv:2308.08090

#4

Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation

Xinshuo Hu, Dongfang Li, Zihao Zheng et al.

40

ICML 2024arXiv:2402.12479

#5

In value-based deep reinforcement learning, a pruned network is a good network

Johan Obando Ceron, Aaron Courville, Pablo Samuel Castro

deep reinforcement learningvalue-based methodssparse trainingnetwork pruning+2

33

ICML 2024arXiv:2401.12200

#6

APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference

Bowen Zhao, Hannaneh Hajishirzi, Qingqing Cao

parameter-efficient fine-tuningstructured pruninglanguage model compressionadaptive parameter tuning+4

28

ECCV 2024arXiv:2407.04616

#7

Isomorphic Pruning for Vision Models

Gongfan Fang, Xinyin Ma, Michael Bi Mi et al.

24

AAAI 2025arXiv:2412.12441

#8

Numerical Pruning for Efficient Autoregressive Models

Xuan Shen, Zhao Song, Yufa Zhou et al.

autoregressive modelsweight pruningattention mechanismmodel compression+3

23

ICLR 2025arXiv:2502.15618

#9

What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity

Gabryel Mason-Williams, Fredrik Dahlqvist

Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing

Qi Le, Enmao Diao, Ziyan Wang et al.

11

ICLR 2025arXiv:2501.15296

#11

You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning

Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty

10

ICML 2025arXiv:2502.06905

#12

Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty

Yeseul Cho, Baekrok Shin, Changmin Kang et al.

9

ICML 2024arXiv:2402.01089

#13

No Free Prune: Information-Theoretic Barriers to Pruning at Initialization

Tanishq Kumar, Kevin Luo, Mark Sellke

lottery ticket hypothesispruning at initializationeffective parameter countmutual information+3

9

ICLR 2025arXiv:2410.09344

#14

DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models

Wenlong Deng, Yize Zhao, Vala Vakilian et al.

8

CVPR 2024arXiv:2404.05621

#15

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning

Matteo Farina, Massimiliano Mancini, Elia Cunegatti et al.

8

ECCV 2024arXiv:2403.16020

#16

PaPr: Training-Free One-Step Patch Pruning with Lightweight ConvNets for Faster Inference

Tanvir Mahmud, Burhaneddin Yaman, Chun-Hao Liu et al.

patch pruninglightweight convnetsvision transformersredundant data elimination+4

7

dynamic pruningspeech foundation modelsmultimodal modelscomputational efficiency+4

#17

Context-aware Dynamic Pruning for Speech Foundation Models

Masao Someki, Yifan Peng, Siddhant Arora et al.

ICLR 2025

7

NEURIPS 2025arXiv:2503.09657

#18

Týr-the-Pruner: Structural Pruning LLMs via Global Sparsity Distribution Optimization

Guanchen Li, Yixing Xu, Zeping Li et al.

structural pruninglarge language modelssparsity distribution optimizationglobal pruning+3

6

ECCV 2024arXiv:2407.14330

#19

Straightforward Layer-wise Pruning for More Efficient Visual Adaptation

Ruizi Han, Jinglei Tang

3

ICLR 2025arXiv:2502.17941

#20

ICP: Immediate Compensation Pruning for Mid-to-high Sparsity

Xin Luo, Fu Xueming, Zihang Jiang et al.

Optimal Brain Apoptosis

Mingyuan Sun, Zheng Fang, Jiaxu Wang et al.

neural network pruningparameter importance estimationhessian matrix analysissecond-order taylor expansion+4

3

AAAI 2025arXiv:2412.12672

#22

Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation

Dongyue Wu, Zilin Guo, Li Yu et al.

CVPR 2025arXiv:2504.02168

#23

MDP: Multidimensional Vision Model Pruning with Latency Constraint

Xinglong Sun, Barath Lakshmanan, Maying Shen et al.

structural pruninglatency-aware pruningmultidimensional pruningtransformer pruning+4

ICML 2025arXiv:2505.23807

#24

DLP: Dynamic Layerwise Pruning in Large Language Models

Yuli Chen, Bo Cheng, Jiale Han et al.

layerwise pruninglarge language modelsmodel compressionparameter-efficient fine-tuning+3

AAAI 2025arXiv:2412.12566

#25

ITP: Instance-Aware Test Pruning for Out-of-Distribution Detection

Haonan Xu, Yang Yang

ECCV 2024arXiv:2403.06417

#26

Enhanced Sparsification via Stimulative Training

Shengji Tang, Weihao Lin, Hancheng Ye et al.

model compressionstructured pruningstimulative trainingself-distillation+4

AAAI 2024arXiv:2402.17862

#27

REPrune: Channel Pruning via Kernel Representative Selection

Mincheol Park, Dongjin Kim, Cheonjun Park et al.

ICCV 2025arXiv:2507.12988

#28

Variance-Based Pruning for Accelerating and Compressing Trained Networks

Uranik Berisha, Jens Mehnert, Alexandru Condurache

structured pruningmodel compressionactivation statisticsvision transformers+4

ICCV 2025arXiv:2412.06458

#29

Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models

Wei Suo, Ji Ma, Mengyang Sun et al.

inference efficiencyvision language modelsparameter pruningtoken pruning+3

ICLR 2025arXiv:2501.12486

#30

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws

Tian Jin, Ahmed Imtiaz Humayun, Utku Evci et al.

sparse pre-traininglarge language modelspruning schedulesscaling laws+4

ICML 2025arXiv:2503.03576

#31

Optimal Decision Tree Pruning Revisited: Algorithms and Complexity

Juha Harviainen, Frank Sommer, Manuel Sorge et al.

NEURIPS 2025arXiv:2505.23195

#32

Resource-Efficient Transformer Pruning for Finetuning of Large Models

Fatih Ilhan, Gong Su, Selim Tekin et al.

Less is More: Unlocking Specialization of Time Series Foundation Models via Structured Pruning

Lifan Zhao, Yanyan Shen, Zhaoyang Liu et al.

NEURIPS 2025arXiv:2509.15497

#34

Backdoor Mitigation via Invertible Pruning Masks

Kealan Dunnett, Reza Arablouei, Volkan Dedeoglu et al.

AAAI 2025arXiv:2408.14721

#35

BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks

Shangqian Gao, Yanfu Zhang, Feihu Huang et al.

EfficientLLaVA: Generalizable Auto-Pruning for Large Vision-language Models

Yinan Liang, Ziwei Wang, Xiuwei Xu et al.

$\mathbb{D}^2$ Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning

Adyasha Maharana, Prateek Yadav, Mohit Bansal

PAT: Pruning-Aware Tuning for Large Language Models

Yijiang Liu, Huanrui Yang, Youxin Chen et al.

AAAI 2025arXiv:2503.06184

#39

Sample-aware Adaptive Structured Pruning for Large Language Models

Jun Kong, Xinge Ma, Jin Wang et al.

model compressiongradient reweightingpruning efficiencylarge language models+3

#40

Targeted Low-rank Refinement: Enhancing Sparse Language Models with Precision

Li Shen, Anke Tang, Yong Luo et al.

Computation and Memory-Efficient Model Compression with Gradient Reweighting

Zhiwei Li, Yuesen Liao, Binrui Wu et al.

NEURIPS 2025

ICML 2025arXiv:2502.04348

#42

Prompt-based Depth Pruning of Large Language Models

Juyun Wee, Minjae Park, Jaeho Lee

NEURIPS 2025arXiv:2509.23931

#43

MEPSI: An MDL-Based Ensemble Pruning Approach with Structural Information

Xiao-Dong Bi, Shao-Qun Zhang, Yuan Jiang

Each Complexity Deserves a Pruning Policy

Hanshi Wang, Yuhao Xu, Zekun Xu et al.

vision-language modelstoken pruningcomplexity-adaptive pruningvisual token redundancy+3

ICML 2024arXiv:2407.04075

#45

Let LLM Tell What to Prune and How Much to Prune

Mingzhe Yang, Sihao Lin, Changlin Li et al.

Sparsest Models Elude Pruning: An Exposé of Pruning’s Current Capabilities

Stephen Zhang, Vardan Papyan

model pruningnetwork sparsitycombinatorial searchoverparameterization effects+3