Sebastian Jonas Neumayer

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In supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this paper, we study a modification of the regularization path for infinite-width 2- ...

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus. Such regularizers give rise to variational denoisers that minimize a convex energy. They rely on few parameters (less than 15,000) and offer a signal ...

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

Robustness and stability of image-reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ( ℓ2 and ℓ1 regulariz ...

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. Unfortunately, it has been shown both theoretically and empi ...

Although Lipschitz-constrained neural networks have many applications in machine learning, the design and training of expressive Lipschitz-constrained networks is very challenging. Since the popular rectified linear-unit networks have provable disadvantage ...

The Lizorkin space is well suited to the study of operators like fractional Laplacians and the Radon transform. In this paper, we show that the space is unfortunately not complemented in the Schwartz space. In return, we show that it is dense in C0(Double- ...

We investigate the learning of a convex patch-based synthesis model for the reconstruction of images. In essence, we propose to learn a dictionary via bilevel optimization for denoising. Using implicit differentiation, we find a closed-form formula of the ...

The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in quality. Unfortunately, these new methods often lack reliability and explainability, and there is a growing interest to address these ...

The reconstruction of images from measured data is an increasing field of research. For highly under-determined problems, template-based image reconstruction provides a way of compensating for the lack of sufficient data. A caveat of this approach is that ...

Sebastian Jonas Neumayer

Chattez avec Graph Search

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Stability of Image-Reconstruction Algorithms

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

Approximation of Lipschitz Functions Using Deep Spline Neural Networks

Explicit representations for Banach subspaces of Lizorkin distributions

Learning a Convex Patch-Based Synthesis Model via Deep Equilibrium

A Neural-Network-Based Convex Regularizer for Inverse Problems

Template-Based Image Reconstruction Facing Different Topologies

Boosting Weakly Convex Ridge Regularizers with Spatial Adaptivity