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New pruning method creates sparse neural networks in one training cycle

By PulseAugur Editorial · [3 sources] ·

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 established methods like the Lottery Ticket Hypothesis (LTH), SNIP, and GraSP on various architectures and datasets. The method demonstrates that high accuracy can be maintained even at extreme sparsity levels, offering an efficient alternative for model compression. AI

IMPACT Offers a more efficient method for model compression, potentially reducing training time and computational resources for AI applications.

RANK_REASON The cluster contains an academic paper detailing a new research method for neural network pruning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Romana Qureshi, Hafida Benhidour, Said Kerrache, Nahlah Aljeraisy ·

    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. Although the Lottery Ticket Hypothesis (LTH) shows that sparse subnetworks can match dense networks w…

  2. arXiv cs.LG TIER_1 English(EN) · Nahlah Aljeraisy ·

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    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…

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