
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Deep neural networks have emerged as very successful tools for image res...
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Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix
Estimating and storing the covariance (or correlation) matrix of highdi...
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Early Stopping in Deep Networks: Double Descent and How to Eliminate it
Overparameterized models, in particular deep networks, often exhibit a ...
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Can Untrained Neural Networks Compete with Trained Neural Networks at Image Reconstruction?
Convolutional Neural Networks (CNNs) are highly effective for image reco...
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Compressive sensing with untrained neural networks: Gradient descent finds the smoothest approximation
Untrained convolutional neural networks have emerged as highly successf...
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Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Generative models, such as GANs, learn an explicit lowdimensional repre...
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DNABased Storage: Models and Fundamental Limits
Due to its longevity and enormous information density, DNA is an attract...
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Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
Convolutional Neural Networks (CNNs) have emerged as highly successful t...
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Leveraging inductive bias of neural networks for learning without explicit human annotations
Classification problems today are typically solved by first collecting e...
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Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients
Normalization layers are widely used in deep neural networks to stabiliz...
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Regularizing linear inverse problems with convolutional neural networks
Deep convolutional neural networks trained on large datsets have emerged...
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Capacity Results for the Noisy Shuffling Channel
Motivated by DNAbased storage, we study the noisy shuffling channel, wh...
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Adaptive Estimation for Approximate kNearestNeighbor Computations
Algorithms often carry out equally many computations for "easy" and "har...
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Superresolution radar imaging via convex optimization
A radar system emits probing signals and records the reflections. Estima...
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Deep Decoder: Concise Image Representations from Untrained Nonconvolutional Networks
Deep neural networks, in particular convolutional neural networks, have ...
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Unsupervised Learning with Stein's Unbiased Risk Estimator
Learning from unlabeled and noisy data is one of the grand challenges of...
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Deep Denoising: RateOptimal Recovery of Structured Signals with a Deep Prior
Deep neural networks provide stateoftheart performance for image deno...
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Approximate Ranking from Pairwise Comparisons
A common problem in machine learning is to rank a set of n items based o...
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DiffuserCam: Lensless Singleexposure 3D Imaging
We demonstrate a compact and easytobuild computational camera for sing...
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The Sample Complexity of Online OneClass Collaborative Filtering
We consider the online oneclass collaborative filtering (CF) problem th...
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Active Ranking from Pairwise Comparisons and when Parametric Assumptions Don't Help
We consider sequential or active ranking of a set of n items based on no...
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Subspace clustering of dimensionalityreduced data
Subspace clustering refers to the problem of clustering unlabeled highd...
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Neighborhood Selection for Thresholdingbased Subspace Clustering
Subspace clustering refers to the problem of clustering highdimensional...
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Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen dence graph (...
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Robust Subspace Clustering via Thresholding
The problem of clustering noisy and incompletely observed highdimension...
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Noisy Subspace Clustering via Thresholding
We consider the problem of clustering noisy highdimensional data points...
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Subspace Clustering via Thresholding and Spectral Clustering
We consider the problem of clustering a set of highdimensional data poi...
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Reinhard Heckel
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