arXiv:2604.13287v2 Announce Type: replace
Abstract: Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre...
By Gabriel Afriat, Xiang Meng, Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder
arXiv:2311.04902v3 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) with billions of parameters are prime targets for network pruning, which removes some model weights without hurt...
By Rocktim Jyoti Das, Mukul Ranjan, Mingjie Sun, Liqun Ma, Zhiqiang Shen
arXiv:2603. 02234v3 Announce Type: replace-cross Abstract: The Strong Lottery Ticket Hypothesis (SLTH) states that large, randomly initialized neural networks contain sparse subnetworks capable of approximating a target function at initialization without training, suggesting that pruning alone is sufficient.
By Davide Ferre' (CNRS, COATI, UniCA, I3S), Fr\'ed\'eric Giroire (I3S, COATI, UniCA), Frederik Mallmann-Trenn (CNRS, COATI, I3S, UniCA), Emanuele Natale (CNRS, COATI, I3S, UniCA)
The paper introduces a Hybrid Quadratic Unconstrained Binary Optimization (QUBO) framework for structured neural network pruning that integrates task‑aware sensitivity metrics (first‑order Taylor and Weight‑Fisher) into the objective’s linear term and optionally uses activation similarity for quadratic interactions. It controls pruning cardinality via a binary search over a capacity incentive rather than an explicit penalty and further refines the pruning mask with a two‑stage QUBO–Tensor‑Train strategy that employs gradient‑free black‑box optimization. Experiments on SIDD image denoising with a Half‑UNet model demonstrate that this Hybrid QUBO outperforms Taylor and L1‑based QUBO baselines in PSNR and SSIM, while also revealing computational and deployment challenges of mask‑based pruning.
By Osama Orabi, Artur Zagitov, Hadi Salloum, Viktor A. Lobachev, Yaroslav Kholodov
arXiv:2412.00143v3 Announce Type: replace-cross
Abstract: Oracle pruning, which selects unimportant weights by minimizing the pruned train loss, has served as the foundation for most neural network p...
By Sicheng Feng, Keda Tao, Huan Wang
arXiv:2609.24401v1 Announce Type: new
Abstract: Structured pruning is a model compression technique that is used to reduce the computational cost of deploying deep neural networks on resource-constra...
By Mindula Illeperuma, Rafael Pina, Charuka Herath, Sharmarke A. Gabayre, Varuna De Silva