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Found 5 papers, 1 papers with code
Coupled Splines for Sparse Curve Fitting
We prove that an optimal solution to the inverse problem is a closed curve with spline components.
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation
Our motivation for defining HTV is to assess the complexity of supervised-learning schemes.
Continuous-Domain Formulation of Inverse Problems for Composite Sparse-Plus-Smooth Signals
We present a novel framework for the reconstruction of 1D composite signals assumed to be a mixture of two additive components, one sparse and the other smooth, given a finite number of linear measurements.
Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant
To that end, we first establish a global bound for the Lipschitz constant of neural networks.
Multi-Kernel Regression with Sparsity Constraint
In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization.
