Article-Journal

This study presents a variant of spectral learning for deep neural networks, where adjusting two sets of eigenvalues for each layer mapping significantly enhances network performance with fewer trainable parameters. This method, inspired by homeostatic plasticity, offers a computationally efficient alternative to conventional training, achieving comparable results with a simpler parameter setup. It also enables the creation of sparser networks with impressive classification abilities.