Machine learning for modular multiplication
<|reference_start|>Machine learning for modular multiplication: Motivated by cryptographic applications, we investigate two machine learning approaches to modular multiplication: namely circular regression and a sequence-to-sequence transformer model. The limited success of both methods demonstrated in our results gives evidence for the hardness of tasks involving modular multiplication upon which cryptosystems are based.<|reference_end|>
@article{lauter2024machine,
    title={Machine learning for modular multiplication},
    author={Kristin Lauter, Cathy Yuanchen Li, Krystal Maughan, Rachel Newton and
  Megha Srivastava},
    journal={arXiv preprint arXiv:2402.19254},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.19254},
    primaryClass={cs.LG cs.CR}
}