Machine learning for modular multiplication
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 29 Feb 2024.
Builds on
9 of 14 references.
References · 9
- 2023Progress measures for grokking via mechanistic interpretabilitycited by 839
- 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 629
- 2023Grokking modular arithmeticcited by 83
- 2023Length Generalization in Arithmetic Transformerscited by 66
- 2021Linear algebra with transformerscited by 63
- 2023Positional Description Matters for Transformers Arithmeticcited by 56
- 2022SALSA: Attacking Lattice Cryptography with Transformerscited by 28
- 2023SALSA PICANTE: a machine learning attack on LWE with binary secretscited by 11
- 2023Gradient Descent Fails to Learn High-frequency Functions and Modular Arithmeticcited by 2
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Citing papers per year: 2024: 2.
2 papers.