s-gai

S-GAI is a spectral geometry-aware initialization framework for one-hidden-layer sigmoidal MLPs that uses class-wise spectral geometry from image data to initialize weights, outperforming random initialization in terms of starting hidden state quality and achieving comparable final accuracy on benchmarks like MNIST and CIFAR-10.