7 citations · 11 across the 17 of their papers we have counts for
21 papers
Compressive Sensing - Introduction and Relations to Deep Learning
Hung-Hsu Chou, Johannes Maly, Holger Rauhut
Compressive sensing predicts that sparse vectors (signals) can be recovered from a small number of linear measurements via efficient algorithms. This finding, which dates back two…
Efficient computation of the singular value decomposition with linear photonic circuits
Johannes Maly, Korbinian Neuner, Samarth Vadia
In light of today's massive data processing, digital computers are reaching fundamental performance limits due to physical limitations and energy consumption. For specific applicat…
Reliable one-bit quantization of bandlimited graph data via single-shot noise shaping
Johannes Maly, Anna Veselovska
Graph data are ubiquitous in natural sciences and machine learning. In this paper, we consider the problem of quantizing graph structured, bandlimited data to few bits per entry wh…
How to induce regularization in linear models: A guide to reparametrizing gradient flow
Hung-Hsu Chou, Johannes Maly, Dominik Stöger
In this work, we analyze the relation between reparametrizations of gradient flow and the induced implicit bias in linear models, which encompass various basic regression tasks. In…
Tuning-free one-bit covariance estimation using data-driven dithering
Sjoerd Dirksen, Johannes Maly
We consider covariance estimation of any subgaussian distribution from finitely many i.i.d. samples that are quantized to one bit of information per entry. Recent work has shown th…
Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
Christian Kümmerle, Johannes Maly
We propose a new algorithm for the problem of recovering data that adheres to multiple, heterogeneous low-dimensional structures from linear observations. Focusing on data matrices…
Plug-in Channel Estimation with Dithered Quantized Signals in Spatially Non-Stationary Massive MIMO Systems
Tianyu Yang, Johannes Maly, Sjoerd Dirksen +1
As the array dimension of massive MIMO systems increases to unprecedented levels, two problems occur. First, the spatial stationarity assumption along the antenna elements is no lo…
A simple approach for quantizing neural networks
Johannes Maly, Rayan Saab
In this short note, we propose a new method for quantizing the weights of a fully trained neural network. A simple deterministic pre-processing step allows us to quantize network l…
Get rid of your constraints and reparametrize: A study in NNLS and implicit bias
Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun +2
Over the past years, there has been significant interest in understanding the implicit bias of gradient descent optimization and its connection to the generalization properties of…
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…
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank
Hung-Hsu Chou, Carsten Gieshoff, Johannes Maly +1
In deep learning, it is common to use more network parameters than training points. In such scenarioof over-parameterization, there are usually multiple networks that achieve zero…
Quantized Compressed Sensing by Rectified Linear Units
Hans Christian Jung, Johannes Maly, Lars Palzer +1
This work is concerned with the problem of recovering high-dimensional signals which belong to a convex set of low-complexity from a small number of q…
Computational approaches to non-convex, sparsity-inducing multi-penalty regularization
Zeljko Kereta, Johannes Maly, Valeriya Naumova
In this work we consider numerical efficiency and convergence rates for solvers of non-convex multi-penalty formulations when reconstructing sparse signals from noisy linear measur…
On Recovery Guarantees for One-Bit Compressed Sensing on Manifolds
Mark A. Iwen, Felix Krahmer, Sara Krause-Solberg +1
This paper studies the problem of recovering a signal from one-bit compressed sensing measurements under a manifold model; that is, assuming that the signal lies on or near a manif…
Analysis of Hard-Thresholding for Distributed Compressed Sensing with One-Bit Measurements
Johannes Maly, Lars Palzer
A simple hard-thresholding operation is shown to be able to recover signals that share a common support of size from $m = \…
Robust Recovery of Low-Rank Matrices with Non-Orthogonal Sparse Decomposition from Incomplete Measurements
Massimo Fornasier, Johannes Maly, Valeriya Naumova
We consider the problem of recovering an unknown effectively -sparse low-rank- matrix with possibly non-orthogonal rank- decomposition from incomplete and inac…