Improving Analog Neural Network Robustness: A Noise-Agnostic Approach with Explainable Regularizations
<|reference_start|>Improving Analog Neural Network Robustness: A Noise-Agnostic Approach with Explainable Regularizations: This work tackles the critical challenge of mitigating "hardware noise" in deep analog neural networks, a major obstacle in advancing analog signal processing devices. We propose a comprehensive, hardware-agnostic solution to address both correlated and uncorrelated noise affecting the activation layers of deep neural models. The novelty of our approach lies in its ability to demystify the "black box" nature of noise-resilient networks by revealing the underlying mechanisms that reduce sensitivity to noise. In doing so, we introduce a new explainable regularization framework that harnesses these mechanisms to significantly enhance noise robustness in deep neural architectures.<|reference_end|>
@article{duque2024improving,
    title={Improving Analog Neural Network Robustness: A Noise-Agnostic Approach
  with Explainable Regularizations},
    author={Alice Duque, Pedro Freire, Egor Manuylovich, Dmitrii Stoliarov,
  Jaroslaw Prilepsky, Sergei Turitsyn},
    journal={arXiv preprint arXiv:2409.08633},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08633},
    primaryClass={cs.LG cs.AI physics.optics}
}