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Found 9 papers, 6 papers with code
Wasserstein Gradient Flows for Moreau Envelopes of f-Divergences in Reproducing Kernel Hilbert Spaces
In this paper, we use the so-called kernel mean embedding to show that the corresponding regularization can be rewritten as the Moreau envelope of some function in the reproducing kernel Hilbert space associated with $K$.
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus.
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks
In supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero.
A Neural-Network-Based Convex Regularizer for Inverse Problems
The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in reconstruction quality.
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community.
Stability of Image-Reconstruction Algorithms
Robustness and stability of image-reconstruction algorithms have recently come under scrutiny.
Approximation of Lipschitz Functions using Deep Spline Neural Networks
Lipschitz-constrained neural networks have many applications in machine learning.
Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
In this paper, we introduce convolutional proximal neural networks (cPNNs), which are by construction averaged operators.
Stabilizing Invertible Neural Networks Using Mixture Models
In this paper, we analyze the properties of invertible neural networks, which provide a way of solving inverse problems.
