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Stable Rank Normalization (SRN) is a weight-normalization scheme which minimizes the stable rank of a linear operator. It simultaneously controls the Lipschitz constant and the stable rank of a linear operator. Stable rank is a softer version of the rank operator and is defined as the squared ratio of the Frobenius norm to the spectral norm.
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
AdaGPR is an adaptive, layer-wise graph convolution model. AdaGPR applies adaptive generalized Pageranks at each layer of a GCNII model by learning to predict the coefficients of generalized Pageranks using sparse solvers.
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank
Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation
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