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08/21/2023
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
We propose to learn non-convex regularizers with a prescribed upper boun...
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11/22/2022
A Neural-Network-Based Convex Regularizer for Image Reconstruction
The emergence of deep-learning-based methods for solving inverse problem...
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10/28/2022
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Lipschitz-constrained neural networks have several advantages compared t...
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08/16/2022
Delaunay-Triangulation-Based Learning with Hessian Total-Variation Regularization
Regression is one of the core problems tackled in supervised learning. R...
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06/17/2022
The Role of Depth, Width, and Activation Complexity in the Number of Linear Regions of Neural Networks
Many feedforward neural networks generate continuous and piecewise-linea...
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04/13/2022
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Lipschitz-constrained neural networks have many applications in machine ...
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03/10/2022
Stable Parametrization of Continuous and Piecewise-Linear Functions
Rectified-linear-unit (ReLU) neural networks, which play a prominent rol...
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12/16/2020