181 citations · 702 across the 49 of their papers we have counts for
8 papers · 1 filter
Theoretical Perspectives on Deep Learning Methods in Inverse Problems
Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues +2
In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resoluti…
Provable Continual Learning via Sketched Jacobian Approximations
Reinhard Heckel
An important problem in machine learning is the ability to learn tasks in a sequential manner. If trained with standard first-order methods most models forget previously learned ta…
Interpolation can hurt robust generalization even when there is no noise
Konstantin Donhauser, Alexandru Ţifrea, Michael Aerni +2
Numerous recent works show that overparameterization implicitly reduces variance for min-norm interpolators and max-margin classifiers. These findings suggest that ridge regulariza…
Unsupervised Learning with Stein's Unbiased Risk Estimator
Christopher A. Metzler, Ali Mousavi, Reinhard Heckel +1
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this w…
Dimensionality-reduced subspace clustering
Reinhard Heckel, Michael Tschannen, Helmut Bölcskei
Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, whose number, orientations, and dim…
Neighborhood Selection for Thresholding-based Subspace Clustering
Reinhard Heckel, Eirikur Agustsson, Helmut Bölcskei
Subspace clustering refers to the problem of clustering high-dimensional data points into a union of low-dimensional linear subspaces, where the number of subspaces, their dimensio…
Compressive Nonparametric Graphical Model Selection For Time Series
Alexander Jung, Reinhard Heckel, Helmut Bölcskei +1
We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian vector random process from finite-length observations. O…
Robust Subspace Clustering via Thresholding
Reinhard Heckel, Helmut Bölcskei
The problem of clustering noisy and incompletely observed high-dimensional data points into a union of low-dimensional subspaces and a set of outliers is considered. The number of…