CoNNect: Connectivity-Based Regularization for Structural Pruning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915341028818944 |
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| author | Franssen, Christian Jiang, Jinyang Peng, Yijie Heidergott, Bernd |
| author_facet | Franssen, Christian Jiang, Jinyang Peng, Yijie Heidergott, Bernd |
| contents | Pruning encompasses a range of techniques aimed at increasing the sparsity of neural networks (NNs). These techniques can generally be framed as minimizing a loss function subject to an $L_0$ norm constraint. This paper introduces CoNNect, a novel differentiable regularizer for sparse NN training that ensures connectivity between input and output layers. We prove that CoNNect approximates $L_0$ regularization, guaranteeing maximally connected network structures while avoiding issues like layer collapse. Moreover, CoNNect is easily integrated with established structural pruning strategies. Numerical experiments demonstrate that CoNNect can improve classical pruning strategies and enhance state-of-the-art one-shot pruners, such as DepGraph and LLM-pruner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00744 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoNNect: Connectivity-Based Regularization for Structural Pruning Franssen, Christian Jiang, Jinyang Peng, Yijie Heidergott, Bernd Machine Learning Pruning encompasses a range of techniques aimed at increasing the sparsity of neural networks (NNs). These techniques can generally be framed as minimizing a loss function subject to an $L_0$ norm constraint. This paper introduces CoNNect, a novel differentiable regularizer for sparse NN training that ensures connectivity between input and output layers. We prove that CoNNect approximates $L_0$ regularization, guaranteeing maximally connected network structures while avoiding issues like layer collapse. Moreover, CoNNect is easily integrated with established structural pruning strategies. Numerical experiments demonstrate that CoNNect can improve classical pruning strategies and enhance state-of-the-art one-shot pruners, such as DepGraph and LLM-pruner. |
| title | CoNNect: Connectivity-Based Regularization for Structural Pruning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.00744 |