Papers › Exploring Prime Number Classification: Achieving High Recall Rate and Rapid...

Exploring Prime Number Classification: Achieving High Recall Rate and Rapid Convergence with Sparse Encoding

30 Jan 2024arXiv:2402.03363archive 2025-07-28

Serin Lee, S. Kim

This paper presents a novel approach at the intersection of machine learning and number theory, focusing on the classification of prime and non-prime numbers. At the core of our research is the development of a highly sparse encoding method, integrated with conventional neural network architectures. This combination has shown promising results, achieving a recall of over 99\% in identifying prime numbers and 79\% for non-prime numbers from an inherently imbalanced sequential series of integers, while exhibiting rapid model convergence before the completion of a single training epoch. We performed training using 10⁶ integers starting from a specified integer and tested on a different range of 2 ×10⁶ integers extending from 10⁶ to 3 ×10⁶, offset by the same starting integer. While constrained by the memory capacity of our resources, which limited our analysis to a span of 3×10⁶, we believe that our study contribute to the application of machine learning in prime number analysis. This work aims to demonstrate the potential of such applications and hopes to inspire further exploration and possibilities in diverse fields.

Code

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections