On Sparsity Averaging

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Whole-brain proton magnetic resonance spectroscopic imaging (H-1-MRSI) is a non-invasive technique for assessing neurochemical distribution in the brain, offering valuable insights into brain functions and neural diseases. It greatly benefits from the impr ...

We formulate computed-tomography reconstruction as a continuous-domain optimization problem with Hessian total variation (HTV) as the regularizer. HTV is a sparsity-promoting regularizer that favors continuous and piecewise-linear functions with few affine ...

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Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

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Limited availability of representative time-to-failure (TTF) trajectories either limits the performance of deep learning (DL)-based approaches on remaining useful life (RUL) prediction in practice or even precludes their application. Generating synthetic d ...

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This toolkit has everything you need to perform imaging with a lensless camera. We make use of a low-cost implementation of DiffuserCam, namely replacing the lens with a piece of tape and using the Raspberry Pi HQ camera sensor. Similar principles and meth ...

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

A computer-implemented method for reconstructing/recovering high-resolution visible light spectral data at a target resolution d, that comprises obtaining a configuration of a low- resolution multi-channel imaging sensor of resolution p, the configuration ...

On Sparsity Averaging

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Rosette Spectroscopic Imaging for Whole-Brain Slab Metabolite Mapping at 7T: Acceleration Potential and Reproducibility

A Continuous-Domain Solution for Computed Tomography with Hessian Total-Variation Regularization

Efficient local linearity regularization to overcome catastrophic overfitting

Towards Trustworthy Deep Learning for Image Reconstruction

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Quantum radio astronomy: Data encodings and quantum image processing

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

Regularization Techniques for Low-Resource Machine Translation

A Neural-Network-Based Convex Regularizer for Inverse Problems

Stability of Image-Reconstruction Algorithms

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Saddle-to-Saddle Dynamics in Diagonal Linear Networks

Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions

Maximum likelihood bolometry for ASDEX upgrade experiments

LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

System and method for transcoding spectral data with task-based optimization via symmetric non-negative matrix factorization