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Structured vs. Unstructured Pruning: An Exponential Gap

arXiv – CS AI|Davide Ferr\'e (CNRS, COATI, UniCA, I3S), Fr\'ed\'eric Giroire (I3S, COATI, UniCA), Emanuele Natale (CNRS, COATI, I3S, UniCA), Frederik Mallmann-Trenn|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Research reveals an exponential gap between structured and unstructured neural network pruning methods. While unstructured weight pruning can approximate target functions with O(d log(1/ε)) neurons, structured neuron pruning requires Ω(d/ε) neurons, demonstrating fundamental limitations of structured approaches.

Key Takeaways

→Unstructured weight pruning significantly outperforms structured neuron pruning for neural network compression.
→Neuron pruning requires exponentially more parameters than weight pruning to achieve the same approximation quality.
→The research isolates intrinsic limitations of structured pruning using ReLU network analysis.
→Findings challenge assumptions about the equivalence of different pruning paradigms in neural networks.
→Results provide theoretical foundation for understanding pruning efficiency in the Strong Lottery Ticket Hypothesis context.

#neural-networks #pruning #machine-learning #optimization #research #efficiency #compression

Read Original →via arXiv – CS AI

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