
Spark Deficient Gabor Frames for Inverse Problems
In this paper, we apply starDigital Gabor Transform in analysis Compres...
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ADMMDAD net: a deep unfolding network for analysis compressed sensing
In this paper, we propose a new deep unfolding neural network based on t...
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Path classification by stochastic linear recurrent neural networks
We investigate the functioning of a classifying biological neural networ...
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Convergence of gradient descent for learning linear neural networks
We study the convergence properties of gradient descent for training dee...
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New challenges in covariance estimation: multiple structures and coarse quantization
In this selfcontained chapter, we revisit a fundamental problem of mult...
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Star DGT: a Robust Gabor Transform for Speech Denoising
In this paper, we address the speech denoising problem, where white Gaus...
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Covariance estimation under onebit quantization
We consider the classical problem of estimating the covariance matrix of...
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Spark Deficient Gabor Frame Provides a Novel Analysis Operator for Compressed Sensing
The analysis sparsity model is a very effective approach in modern Compr...
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Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank
We provide an explicit analysis of the dynamics of vanilla gradient desc...
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Generalization bounds for deep thresholding networks
We consider compressive sensing in the scenario where the sparsity basis...
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Unfolding recurrence by Green's functions for optimized reservoir computing
Cortical networks are strongly recurrent, and neurons have intrinsic tem...
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Weighted Optimization: better generalization by smoother interpolation
We provide a rigorous analysis of how implicit bias towards smooth inter...
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Sparse recovery in bounded Riesz systems with applications to numerical methods for PDEs
We study sparse recovery with structured random measurement matrices hav...
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Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
We study the convergence of gradient flows related to learning deep line...
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On the geometry of polytopes generated by heavytailed random vectors
We study the geometry of centrallysymmetric random polytopes, generated...
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Holger Rauhut
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