Confident magnitude-based neural network pruning
<|reference_start|>Confident magnitude-based neural network pruning: Pruning neural networks has proven to be a successful approach to increase the efficiency and reduce the memory storage of deep learning models without compromising performance. Previous literature has shown that it is possible to achieve a sizable reduction in the number of parameters of a deep neural network without deteriorating its predictive capacity in one-shot pruning regimes. Our work builds beyond this background in order to provide rigorous uncertainty quantification for pruning neural networks reliably, which has not been addressed to a great extent in previous literature focusing on pruning methods in computer vision settings. We leverage recent techniques on distribution-free uncertainty quantification to provide finite-sample statistical guarantees to compress deep neural networks, while maintaining high performance. Moreover, this work presents experiments in computer vision tasks to illustrate how uncertainty-aware pruning is a useful approach to deploy sparse neural networks safely.<|reference_end|>
@article{alvarez2024confident,
    title={Confident magnitude-based neural network pruning},
    author={Joaquin Alvarez},
    journal={arXiv preprint arXiv:2408.04759},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04759},
    primaryClass={cs.LG}
}