Pruning

AAAI 2025arXiv:2412.12441

#2

Numerical Pruning for Efficient Autoregressive Models

Xuan Shen, Zhao Song, Yufa Zhou et al.

22

ECCV 2024arXiv:2407.04616

#3

Isomorphic Pruning for Vision Models

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

20

ICLR 2025arXiv:2501.15296

#4

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

Gabryel Mason-Williams, Fredrik Dahlqvist

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

Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty

9

ICLR 2025arXiv:2410.09344

#6

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

Wenlong Deng, Yize Zhao, Vala Vakilian et al.

8

ECCV 2024arXiv:2403.16020

#7

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

#8

Context-aware Dynamic Pruning for Speech Foundation Models

Masao Someki, Yifan Peng, Siddhant Arora et al.

ICLR 2025

7

NEURIPS 2025arXiv:2503.09657

#9

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

ICLR 2025arXiv:2502.17941

#10

Optimal Brain Apoptosis

Mingyuan Sun, Zheng Fang, Jiaxu Wang et al.

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

3

ECCV 2024arXiv:2403.06417

#11

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

Xin Luo, Fu Xueming, Zihang Jiang et al.

Enhanced Sparsification via Stimulative Training

Shengji Tang, Weihao Lin, Hancheng Ye et al.

model compressionstructured pruningstimulative trainingself-distillation+4

2

ICCV 2025arXiv:2412.06458

#13

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

CVPR 2025arXiv:2504.02168

#14

MDP: Multidimensional Vision Model Pruning with Latency Constraint

Xinglong Sun, Barath Lakshmanan, Maying Shen et al.

structural pruninglatency-aware pruningmultidimensional pruningtransformer pruning+4

ICCV 2025arXiv:2507.12988

#15

Variance-Based Pruning for Accelerating and Compressing Trained Networks

Uranik Berisha, Jens Mehnert, Alexandru Condurache

structured pruningmodel compressionactivation statisticsvision transformers+4

ICLR 2025arXiv:2501.12486

#16

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

ECCV 2024arXiv:2407.14330

#17

Straightforward Layer-wise Pruning for More Efficient Visual Adaptation

Ruizi Han, Jinglei Tang

AAAI 2025arXiv:2412.12672

#18

Structural Pruning via Spatial-aware Information Redundancy for Semantic Segmentation

Dongyue Wu, Zilin Guo, Li Yu et al.

model compressiongradient reweightingpruning efficiencylarge language models+3

#19

Computation and Memory-Efficient Model Compression with Gradient Reweighting

Zhiwei Li, Yuesen Liao, Binrui Wu et al.

NEURIPS 2025

ICLR 2025arXiv:2502.15618

#20

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

Qi Le, Enmao Diao, Ziyan Wang et al.

NEURIPS 2025arXiv:2505.23195

#21

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

Lifan Zhao, Yanyan Shen, Zhaoyang Liu et al.

ICML 2025arXiv:2502.06905

#22

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

Yinan Liang, Ziwei Wang, Xiuwei Xu et al.

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

Yeseul Cho, Baekrok Shin, Changmin Kang et al.

ICML 2024arXiv:2401.12200

#24

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

ICML 2024arXiv:2402.09025

#25

SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Jiwon Song, Kyungseok Oh, Taesu Kim et al.

llm pruningtransformer block redundancymodel compressioninference acceleration+2

ICML 2024arXiv:2407.04075

#26

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

Stephen Zhang, Vardan Papyan

model pruningnetwork sparsitycombinatorial searchoverparameterization effects+3

AAAI 2025arXiv:2412.12566

#27

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

Haonan Xu, Yang Yang

ICML 2024arXiv:2406.02924

#28

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

Adyasha Maharana, Prateek Yadav, Mohit Bansal

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

ICML 2025arXiv:2503.03576

#30

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

Li Shen, Anke Tang, Yong Luo et al.

Optimal Decision Tree Pruning Revisited: Algorithms and Complexity

Juha Harviainen, Frank Sommer, Manuel Sorge et al.

AAAI 2024arXiv:2402.17862

#32

REPrune: Channel Pruning via Kernel Representative Selection

Mincheol Park, Dongjin Kim, Cheonjun Park et al.

AAAI 2025arXiv:2503.06184

#33

Resource-Efficient Transformer Pruning for Finetuning of Large Models

Fatih Ilhan, Gong Su, Selim Tekin et al.

Sample-aware Adaptive Structured Pruning for Large Language Models

Jun Kong, Xinge Ma, Jin Wang et al.

ICML 2025arXiv:2502.04348

#35

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

Shangqian Gao, Yanfu Zhang, Feihu Huang et al.

Prompt-based Depth Pruning of Large Language Models

Juyun Wee, Minjae Park, Jaeho Lee

ICML 2024arXiv:2402.12479

#37

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

ICML 2024arXiv:2402.01089

#38

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

AAAI 2025arXiv:2408.14721

#39

Let LLM Tell What to Prune and How Much to Prune

Mingzhe Yang, Sihao Lin, Changlin Li et al.

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

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

PAT: Pruning-Aware Tuning for Large Language Models

Yijiang Liu, Huanrui Yang, Youxin Chen et al.

CVPR 2024arXiv:2404.05621

#42

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning

Matteo Farina, Massimiliano Mancini, Elia Cunegatti et al.

ICML 2025arXiv:2505.23807

#43

DLP: Dynamic Layerwise Pruning in Large Language Models

Yuli Chen, Bo Cheng, Jiale Han et al.

NEURIPS 2025arXiv:2509.23931

#44

Each Complexity Deserves a Pruning Policy

Hanshi Wang, Yuhao Xu, Zekun Xu et al.

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

AAAI 2024arXiv:2308.08090

#45

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

Xinshuo Hu, Dongfang Li, Zihao Zheng et al.

NEURIPS 2025arXiv:2509.15497

#46

Backdoor Mitigation via Invertible Pruning Masks

Kealan Dunnett, Reza Arablouei, Volkan Dedeoglu et al.