Practically Solving LPN in High Noise Regimes Faster Using Neural Networks

ePrint Report: Practically Solving LPN in High Noise Regimes Faster Using Neural Networks

Haozhe Jiang, Kaiyue Wen, Yilei Chen

We conduct a systematic study of solving the learning parity with noise problem (LPN) using neural networks. Our main contribution is designing families of two-layer neural networks that practically outperform classical algorithms in high-noise, low-dimension regimes. We consider three settings where the numbers of LPN samples are abundant, very limited, and in between. In each setting we provide neural network models …

algorithms chen eprint report high low main networks neural networks noise numbers problem report settings study