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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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03/31/2023
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
In supervised learning, the regularization path is sometimes used as a c...
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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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06/14/2022
Stability of image reconstruction algorithms
Robustness and stability of image reconstruction algorithms have recentl...
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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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11/04/2020
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
In this paper, we introduce convolutional proximal neural networks (cPNN...
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09/07/2020
Stabilizing Invertible Neural Networks Using Mixture Models
In this paper, we analyze the properties of invertible neural networks, ...
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06/29/2020
Transfer Operators from Optimal Transport Plans for Coherent Set Detection
The topic of this study lies in the intersection of two fields. One is r...
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12/19/2019