
The Separation Capacity of Random Neural Networks
Neural networks with random weights appear in a variety of machine learn...
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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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Covariance estimation under onebit quantization
We consider the classical problem of estimating the covariance matrix of...
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Binarized JohnsonLindenstrauss embeddings
We consider the problem of encoding a set of vectors into a minimal numb...
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Statistical postprocessing of wind speed forecasts using convolutional neural networks
Current statistical postprocessing methods for probabilistic weather fo...
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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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Robust onebit compressed sensing with partial circulant matrices
We present optimal sample complexity estimates for onebit compressed se...
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Robust onebit compressed sensing with nonGaussian measurements
We study memoryless onebit compressed sensing with nonGaussian measure...
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Dimensionality reduction with subgaussian matrices: a unified theory
We present a theory for Euclidean dimensionality reduction with subgauss...
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Toward a unified theory of sparse dimensionality reduction in Euclidean space
Let Φ∈R^m× n be a sparse JohnsonLindenstrauss transform [KN14] with s n...
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Sjoerd Dirksen
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