New pruning method creates sparse neural networks in one training cycle

By PulseAugur Editorial · [1 sources] · 2026-06-10 16:17

Researchers have developed a new method for creating sparse neural networks in a single training cycle, a significant improvement over existing techniques that require multiple cycles. This progressive magnitude-based pruning approach gradually increases sparsity during training, achieving competitive or superior accuracy compared to iterative methods like the Lottery Ticket Hypothesis (LTH) on benchmark datasets. For instance, on CIFAR-10, the method reached 95.12% accuracy on ResNet-18 at 72.9% sparsity, outperforming LTH's reported accuracy. AI

IMPACT Offers a more efficient way to create smaller, faster neural networks by reducing training time.

RANK_REASON The cluster contains an academic paper detailing a new method for neural network pruning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

paper
other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

arXiv cs.LG TIER_1 English(EN) · Nahlah Aljeraisy · 2026-06-10 16:17

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 ite…

COVERAGE [1]

Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning

RELATED ENTITIES

RELATED TOPICS