559 citations · 1.5k across the 55 of their papers we have counts for
9 papers · 1 filter
More is Less: Inducing Sparsity via Overparameterization
Hung-Hsu Chou, Johannes Maly, Holger Rauhut
In deep learning it is common to overparameterize neural networks, that is, to use more parameters than training samples. Quite surprisingly training the neural network via (stocha…
Spark Deficient Gabor Frames for Inverse Problems
Vasiliki Kouni, Holger Rauhut
In this paper, we apply star-Digital Gabor Transform in analysis Compressed Sensing and speech denoising. Based on assumptions on the ambient dimension, we produce a window vector…
ADMM-DAD net: a deep unfolding network for analysis compressed sensing
Vasiliki Kouni, Georgios Paraskevopoulos, Holger Rauhut +1
In this paper, we propose a new deep unfolding neural network based on the ADMM algorithm for analysis Compressed Sensing. The proposed network jointly learns a redundant analysis…
Path classification by stochastic linear recurrent neural networks
Wiebke Bartolomaeus, Youness Boutaib, Sandra Nestler +1
We investigate the functioning of a classifying biological neural network from the perspective of statistical learning theory, modelled, in a simplified setting, as a continuous-ti…
Convergence of gradient descent for learning linear neural networks
Gabin Maxime Nguegnang, Holger Rauhut, Ulrich Terstiege
We study the convergence properties of gradient descent for training deep linear neural networks, i.e., deep matrix factorizations, by extending a previous analysis for the related…
New challenges in covariance estimation: multiple structures and coarse quantization
Johannes Maly, Tianyu Yang, Sjoerd Dirksen +2
In this self-contained chapter, we revisit a fundamental problem of multivariate statistics: estimating covariance matrices from finitely many independent samples. Based on massive…