arXiv AI
Jun 9
Structured Neuron Pruning in Deep Neural Networks Using Multi-Armed Bandits
arXiv:2606. 07615v1 Announce Type: cross Abstract: Deep neural networks often contain redundant hidden units.
Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits
arXiv:2607. 22564v1 Announce Type: new Abstract: Convolutional neural networks often contain redundant feature maps that increase storage and inference cost.
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arXiv AI
Sep 10
Damage-Aware Bandit Pruning for Vision and Language Transformers
The paper introduces a structured post‑training pruning method for vision and language transformers called Damage‑Aware Bandit Pruning. It treats the selection of functional units (attention heads and MLP channel groups) as a multi‑armed bandit problem, using paired damage (masked loss minus base loss) as a reward to guide either UCB or Thompson Sampling policies. Experiments on a range of models (GPT‑2, OPT, Pythia, Qwen2.5, SmolLM2, ViT‑B/16, DeiT‑Tiny, Swin‑Tiny) show that the bandit approaches generally reduce degradation compared to budgeted‑greedy baselines, with statistically significant improvements in most comparisons.
arXiv Machine Learning
Jun 9
OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
arXiv:2606. 08574v1 Announce Type: new Abstract: Data pruning (DP), as an oft-stated strategy to alleviate heavy training burdens, reduces the volume of training samples according to a well-defined pruning method while striving for near-lossless performance.
arXiv Computer Vision
Sep 10
Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs
arXiv:2609.10346v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existin...
arXiv Computer Vision
Sep 10
One Loop, Two Gains: Can Active Learning win the Lottery for Free?
arXiv:2609.10311v1 Announce Type: cross Abstract: The lottery ticket hypothesis posits the existence of winning tickets: sparse subnetworks that, when trained in isolation from their original initial...
arXiv Machine Learning
Jun 11
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
arXiv:2606. 12278v1 Announce Type: cross Abstract: Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance.
Hugging Face Trending Papers
Jun 10
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance. Although the Lottery Ticket Hypothesis (LTH) shows that sparse subnetworks can match dense networks when trained from suitable initializations, its iterative pruning procedure requires multiple complete training cycles.
arXiv Machine Learning
Sep 22
Task-Aware Hybrid QUBO Optimization for Structured Neural Network Pruning
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.
arXiv Machine Learning
Jun 11
RCAP: Robust, Class-Aware, Probabilistic Dynamic Dataset Pruning
arXiv:2606. 11761v1 Announce Type: new Abstract: Dynamic data pruning techniques aim to reduce computational cost while minimizing information loss by periodically selecting representative subsets of input data during model training.
arXiv Machine Learning
Sep 1
MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models
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...
arXiv Machine Learning
Jul 21
NIRVANA: Structured Pruning Reimagined for Large Language Model Compression
arXiv:2509. 14230v2 Announce Type: replace Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities.
arXiv Computation and Language
Aug 31
Pruning Laws for Large Language Models
arXiv:2504.04342v2 Announce Type: replace Abstract: Scaling up model parameters and training data consistently improves the performance of large language models (LLMs), but at the cost of rapidly gro...