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NEPENTHE: Entropy-Based Pruning as a Neural Network Depth's Reducer
April 29, 2024, 4:41 a.m. | Zhu Liao, Victor Qu\'etu, Van-Tam Nguyen, Enzo Tartaglione
cs.LG updates on arXiv.org arxiv.org
Abstract: While deep neural networks are highly effective at solving complex tasks, their computational demands can hinder their usefulness in real-time applications and with limited-resources systems. Besides, for many tasks it is known that these models are over-parametrized: neoteric works have broadly focused on reducing the width of these networks, rather than their depth. In this paper, we aim to reduce the depth of over-parametrized deep neural networks: we propose an eNtropy-basEd Pruning as a nEural …
abstract applications arxiv computational cs.ai cs.lg entropy hinder network networks neural network neural networks pruning real-time real-time applications resources systems tasks type while
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