Heterogeneous Pruning Scores

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Period ending 2026-09-14

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Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning
Sep 21, 2026Mindula Illeperuma, Rafael Pina, Charuka Herath +2Heterogeneous Pruning Scores
LILA: Calibration-Free Structured Pruning of Large Language Models via Latent Spectral Geometry
Sep 11, 2026Sankar Behera, Dhruv Singh, Anshika Agnihotri +3Large Language Model Compression Heterogeneous Pruning Scores
One Loop, Two Gains: Can Active Learning win the Lottery for Free?
Sep 9, 2026Benedikt Tscheschner, Eduardo Veas, Marc MasanaActive Learning Heterogeneous Pruning Scores
Forward-Free LLM Depth Pruning via Weight Redundancy
Sep 9, 2026Vincent-Daniel Yun, Woosang LimHeterogeneous Pruning Scores Large Language Model Compression
Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning
Sep 3, 2026Pablo Torrijos, José A. Gámez, José M. Puerta +1Bayesian Networks Genetic Algorithms
TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information
Aug 31, 2026Dain Kwon, Kanghyun Choi, Hyeyoon Lee +4Weight-Only Quantization Quantization
PruneShift: A Framework for Evaluating Decision Reliability in Structured Pruning
Aug 30, 2026Hao Ye, Gaopeng ZhangHeterogeneous Pruning Scores Surrogate Performance
Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization
Aug 13, 2026Palaash Goel, Ayan Sengupta, Akshay Nambi +1Large Language Model Compression Heterogeneous Pruning Scores
Domain-Aware Pruning: Sparsity and Domain Generalization via Regularized Probabilistic Masking
Aug 9, 2026Parham Sazdar, Mostafa Tavassolipour, Reshad HosseiniMultimodal Domain Generalization Heterogeneous Pruning Scores
APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning
Aug 6, 2026Sadegh Jafari, Mohiuddin Bilwal, Fan Zhou +2Mixed-Precision Quantization Heterogeneous Pruning Scores
BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning
Aug 5, 2026Sajib Hossain, Md Kamrus Samad, Anan Ghosh +2Bert-Based Models Bangla
StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning
Aug 5, 2026Timo Bartels, Danish Nazir, Jan Piewek +2Dense Semantic Segmentation Heterogeneous Pruning Scores
Understanding Fault Tolerance of Adversarially Robust Pruned Models
Aug 4, 2026Manali Dangarikar, Cory MerkelModern Deep Networks Heterogeneous Pruning Scores
TaskPress: Query-Agnostic KV Cache Compression via Task-Guided Pruning
Aug 4, 2026Wonpyo Park, Seung-won HwangKey-Value Cache Compression Key-Value Cache Eviction
ATFlash: Per-RoPE-Wavelength Attention Windows for Compute/Memory-Efficient LLM Inference
Aug 3, 2026Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +1Quadratic Attention Rotary Position Embedding
F-WANDA: Fisher-Reweighted Post-Training Pruning for Sustainable Deployment of Large Language Models
Aug 1, 2026Himanshu MishraLarge Language Model Compression Heterogeneous Pruning Scores
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Jul 30, 2026Haozhe Hu, Hao Wu, Peiran Yin +3Heterogeneous Pruning Scores LLM Inference Efficiency
Unified Static-Dynamic Pruning for Efficient LLM Inference
Jul 24, 2026Jinhyeok Kim, Yejoon Lee, Jaeyoung DoLLM Inference Efficiency Heterogeneous Pruning Scores
PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption
Jul 20, 2026Sahaj Majavdia, Mahdi TaheriHomomorphic Encryption Neural Network Verification
Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition
Jul 16, 2026Tong Jin, Yunpeng Liu, Shuyu Hu +3Visual Place Recognition Heterogeneous Pruning Scores
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
Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention
Jul 9, 2026Ryota Kobayashi, Tsubasa Hirakawa, Takayoshi Yamashita +4Heterogeneous Pruning Scores Pruning
PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning
Jul 8, 2026Yazdan Jamshidi, Alexey ShvetsHeterogeneous Pruning Scores Large Language Model Compression
Hardware-aware Graph Neural Networks prunning for embedded event-based vision
Jul 7, 2026Piotr Wzorek, Kamil Jeziorek, Tomasz KryjakGraph Neural Networks Heterogeneous Pruning Scores
Post-Training Pruning for Diffusion Transformers
Jul 1, 2026Chengzhi Hu, Xuewen Liu, Jing Zhang +3Multimodal Diffusion Transformers Heterogeneous Pruning Scores
Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT
Jun 25, 2026Jinghan Wang, Yanjun Chen, Wei Zhang +3Heterogeneous Pruning Scores LLM Inference Efficiency
Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization
Jun 22, 2026Lucas Heublein, Mark Deutel, Axel Plinge +1Graph Neural Network Inference Real-Time Inference
Complementary Attention Head Pruning for Efficient Transformers
Jun 17, 2026Yaniv Livertovsky, Shahar Somin, Gonen SingerTransformer Attention Heterogeneous Pruning Scores
S4oP: Operator-level Pruning of Structured State Space Models for Resource-Constrained Devices
Jun 16, 2026Marco Deano, Filippo Ziche, Nicola BombieriState Space Models Heterogeneous Pruning Scores
Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression
Jun 16, 2026Yifu Ding, Jiacheng Wang, Ge Yang +4Mixture-Of-Expert Inference Mixture-of-Experts Models
DCP-Prune: Ultra-Low Token Pruning with Distribution Consistency Preservation
Jun 15, 2026Xifeng Xue, Xiaokang Wang, Zirui Li +2Visual Token Pruning Heterogeneous Pruning Scores
DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction
Jun 13, 2026Fuyan Zhang, Yuqi Li, Qing Xu +2Spatiotemporal Prediction Heterogeneous Pruning Scores
Squeeze-Release: Iterative Pruning with Exact Structural Minimization
Jun 12, 2026Roman Denkin, Ida Akerholm, Prashant Singh +1Heterogeneous Pruning Scores Pruning
Selecting Samples on Graphs: A Unified Dataset Pruning Framework for Lossless Training Acceleration
Jun 11, 2026Dongyue Wu, Zilin Guo, Xiaoyu Li +4Heterogeneous Pruning Scores Graph Representation Learning
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
Jun 10, 2026Romana Qureshi, Hafida Benhidour, Said Kerrache +1Heterogeneous Pruning Scores Sparse Deep Neural Network Accelerators
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning
Jun 10, 2026Atif Hassan, Swanand Khare, Jiaul H. PaikHeterogeneous Pruning Scores Extreme Class Imbalance
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
Jun 8, 2026Haozhe Hu, Hao Wu, Anhao Zhao +4Heterogeneous Pruning Scores LLM Inference Efficiency
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
Jun 7, 2026Chenhan Jin, Shengze Xu, Qingsong Wang +3Heterogeneous Pruning Scores Pruning
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
Jun 5, 2026Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi +1Mixed-Precision Quantization Weight-Only Quantization
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
Jun 2, 2026Mihail Stoian, Mark Gerarts, Pascal Ginter +3Skip Heterogeneous Pruning Scores
CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search
Jun 1, 2026Cheonjun ParkHeterogeneous Pruning Scores Large Language Model Post-Training
Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits
May 29, 2026Salem Ameen, Sunil VaderaModern Deep Networks Heterogeneous Pruning Scores
PINE: Pruning Boosted Tree Ensembles with Conformal In-Distribution Prediction Equivalence
May 27, 2026Haruki Yajima, Yusuke MatsuiDecision Trees Heterogeneous Pruning Scores
Resource-Constrained Affect Modelling via Variance Regularisation Pruning
May 26, 2026Kosmas Pinitas, Konstantinos KatsifisAffective Computing Heterogeneous Pruning Scores
Label-Efficient Dataset Pruning via Semi-Supervised Pseudo-Labeling
May 22, 2026Yeseul Cho, Baekrok Shin, Changmin Kang +1Pseudo-Labeling Heterogeneous Pruning Scores
Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation
May 21, 2026Fabian Morelli, Stephan EcksteinNeural Network Ensemble
Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding
May 19, 2026Yuhao Shen, Tianyu Liu, Xinyi Hu +9Speculative Decoding Heterogeneous Pruning Scores
Prune, Update and Trim: Robust Structured Pruning for Large Language Models
May 18, 2026Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidt-ThiemeHeterogeneous Pruning Scores Pruning
On the Stability of Growth in Structural Plasticity
May 14, 2026Lute Lillo, Nick CheneyPlasticity Growth
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 TamHeterogeneous Pruning Scores Model Selection
Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments
May 11, 2026Danilo Brajovic, David A. Kreplin, Marco F. HuberHeterogeneous Pruning Scores Pruning
Relative Kinetic Utility for Reasoning-Aware Structural Pruning in Large Language Models
May 9, 2026Tianhao QianHeterogeneous Pruning Scores Large Models
Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning
May 9, 2026Chen Wang, Siyu Hu, Guangming Tan +1Equivariant Neural Networks Wireless Foundation Models
Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions
May 8, 2026Boyu Shi, Chang Liu, ChuanBao Gao +2Large Language Model Performance Heterogeneous Pruning Scores
PACE: Prune-And-Compress Ensemble Models
May 7, 2026Fabian Akkerman, Julien Ferry, Théo Guyard +1Ensemble Heterogeneous Pruning Scores
Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI
May 2, 2026Plawan Kumar Rath, Rahul MaliakkalHeterogeneous Pruning Scores Large Language Model Bias
Random Cloud: Finding Minimal Neural Architectures Without Training
Apr 29, 2026Javier Gil BlázquezNeural Architecture Search Heterogeneous Pruning Scores
Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling
Apr 28, 2026Ocean Monjur, Shahriar Kabir Nahin, Anshuman ChhabraHeterogeneous Pruning Scores Test-Time Scaling
DepthKV: Layer-Dependent KV Cache Pruning for Long-Context LLM Inference
Apr 27, 2026Zahra Dehghanighobadi, Asja FischerKey-Value Cache Eviction LLM Inference Efficiency

Sep 21, 2026 · cs.LGJ/K move · Enter open · S save

Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning

Mindula Illeperuma, Rafael Pina, Charuka Herath, Sharmarke A. Gabayre+1

Structured pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constrained devices. Popular methods of pruning rely on opaque heuristics or weight-based criteria that give no indication as to the structural dependencies in the network. To address these limitations we present Artificial Structure Function Search (ASF-S): a novel structured pruning framework. ASF-S utilizes Principle Gradient Importance (PGI): a novel prune-candidate selection criteria that is inspired by structure-function relationships in the brain. By ensuring the pruned structure of the model respects topographical organization of the output layer, we define Artificial Functional Connectivity (AFC) for artificial neural networks. AFC provides evidence to demonstrate that accurate smaller networks can be found using careful prune candidate selection criteria. We present results for PGI as a selection criterion and for ASF-S as a pruning framework against recent benchmarks, demonstrating that our method yields model variants with 70% parameter reduction, that can recover baseline accuracy without re-training the pruned layers.