Output Range Analysis for Deep Neural Networks based on Simulated Annealing Processes
<|reference_start|>Output Range Analysis for Deep Neural Networks based on Simulated Annealing Processes: This paper tackles the challenging problem of output range estimation for Deep Neural Networks (DNNs), introducing a novel algorithm based on Simulated Annealing (SA). Our approach addresses the lack of local geometric information and high non-linearity in DNNs, making it versatile across various architectures, especially Residual Neural Networks (ResNets). We present a straightforward, implementation-friendly algorithm that avoids restrictive assumptions about network architecture. Through theoretical analysis and experimental evaluations, including tests on the Ackley function, we demonstrate our algorithm's effectiveness in navigating complex, non-convex surfaces and accurately estimating DNN output ranges. Futhermore, the Python codes of this experimental evaluation that support our results are available in our GitHub repository (https://github.com/Nicerova7/output-range-analysis-for-deep-neural-networks-with-simulated-annealing).<|reference_end|>
@article{rojas2024output,
    title={Output Range Analysis for Deep Neural Networks based on Simulated
  Annealing Processes},
    author={Helder Rojas, Nilton Rojas, Espinoza J. B., Luis Huamanchumo},
    journal={arXiv preprint arXiv:2407.02700},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02700},
    primaryClass={cs.LG math.PR stat.ML}
}