7 citations · 11 across the 17 of their papers we have counts for
6 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…
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…
Covariance estimation under one-bit quantization
Sjoerd Dirksen, Johannes Maly, Holger Rauhut
We consider the classical problem of estimating the covariance matrix of a subgaussian distribution from i.i.d. samples in the novel context of coarse quantization, i.e., instead o…
Robust Sensing of Low-Rank Matrices with Non-Orthogonal Sparse Decomposition
Johannes Maly
We consider the problem of recovering an unknown low-rank matrix X with (possibly) non-orthogonal, effectively sparse rank-1 decomposition from measurements y gathered in a linear…
Structural Sparsity in Multiple Measurements
Florian Boßmann, Sara Krause-Solberg, Johannes Maly +1
We propose a novel sparsity model for distributed compressed sensing in the multiple measurement vectors (MMV) setting. Our model extends the concept of row-sparsity to allow more…
Quantitative approximation results for complex-valued neural networks
A. Caragea, D. G. Lee, J. Maly +2
Until recently, applications of neural networks in machine learning have almost exclusively relied on real-valued networks. It was recently observed, however, that complex-valued n…