Practically Solving LPN in High Noise Regimes Faster Using Neural Networks. (arXiv:2303.07987v1 [cs.LG])

Web: http://arxiv.org/abs/2303.07987

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 that solve LPN as fast as
possible. For some settings we are also able to provide theories that explain …

high networks neural networks