HIGHLIGHTS
- who: Hessian Total-Variation Regularization et al. from the (UNIVERSITY) have published the research: Learning of Continuous and Piecewise-Linear Functions with Hessian Total-Variation Regularization, in the Journal: (JOURNAL)
- what: This is in contrast with the numerous hyperparameters found in neural_networks such as the choice of architecture and its components, learning rate schemes, and batch size, among others; 4) an improved model interpretability since the authors provide a linear parametrization for the learned CPWL mapping. The authors demonstrate the advantages of the pipeline by comparing it to other existing learning methods. The authors demonstrate . . .
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