Reinhard Heckel

research

∙ 08/11/2023

Embracing Errors is More Efficient than Avoiding Them through Constrained Coding for DNA Data Storage

DNA is an attractive medium for digital data storage. When data is store...

0 Franziska Weindel, et al. ∙

research

∙ 08/05/2023

K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets

Although deep learning (DL) methods are powerful for solving inverse pro...

0 Frederic Wang, et al. ∙

research

∙ 08/05/2023

Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks

We investigate to what extent it is possible to solve linear inverse pro...

0 Stefan Bamberger, et al. ∙

research

∙ 07/24/2023

Learning Provably Robust Estimators for Inverse Problems via Jittering

Deep neural networks provide excellent performance for inverse problems ...

0 Anselm Krainovic, et al. ∙

research

∙ 05/30/2023

Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods

Supervised training of deep neural networks on pairs of clean image and ...

0 Tobit Klug, et al. ∙

research

∙ 05/11/2023

Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction

In this paper, we propose an approach for cardiac magnetic resonance ima...

0 Johannes F. Kunz, et al. ∙

research

∙ 03/20/2023

Zero-Shot Noise2Noise: Efficient Image Denoising without any Data

Recently, self-supervised neural networks have shown excellent image den...

0 Youssef Mansour, et al. ∙

research

∙ 11/10/2022

Information-Theoretic Foundations of DNA Data Storage

Due to its longevity and enormous information density, DNA is an attract...

0 Ilan Shomorony, et al. ∙

research

∙ 10/20/2022

Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization

Machine learning systems are often applied to data that is drawn from a ...

0 Daniel LeJeune, et al. ∙

research

∙ 10/09/2022

Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examples

Modern image classifiers achieve high predictive accuracy, but the predi...

0 Fatih Furkan Yilmaz, et al. ∙

research

∙ 09/27/2022

Scaling Laws For Deep Learning Based Image Reconstruction

Deep neural networks trained end-to-end to map a measurement of a (noisy...

0 Tobit Klug, et al. ∙

research

∙ 06/29/2022

Theoretical Perspectives on Deep Learning Methods in Inverse Problems

In recent years, there have been significant advances in the use of deep...

0 Jonathan Scarlett, et al. ∙

research

∙ 06/03/2022

Regularization-wise double descent: Why it occurs and how to eliminate it

The risk of overparameterized models, in particular deep neural networks...

0 Fatih Furkan Yilmaz, et al. ∙

research

∙ 02/04/2022

Image-to-Image MLP-mixer for Image Reconstruction

Neural networks are highly effective tools for image reconstruction prob...

0 Youssef Mansour, et al. ∙

research

∙ 12/09/2021

Provable Continual Learning via Sketched Jacobian Approximations

An important problem in machine learning is the ability to learn tasks i...

0 Reinhard Heckel, et al. ∙

research

∙ 12/02/2021

Achieving the Capacity of a DNA Storage Channel with Linear Coding Schemes

Due to the redundant nature of DNA synthesis and sequencing technologies...

0 Kel Levick, et al. ∙

research

∙ 09/24/2021

Untrained Graph Neural Networks for Denoising

A fundamental problem in signal processing is to denoise a signal. While...

0 Samuel Rey, et al. ∙

research

∙ 08/05/2021

Interpolation can hurt robust generalization even when there is no noise

Numerous recent works show that overparameterization implicitly reduces ...

0 Konstantin Donhauser, et al. ∙

research

∙ 06/28/2021

Data augmentation for deep learning based accelerated MRI reconstruction with limited data

Deep neural networks have emerged as very successful tools for image res...

72 Zalan Fabian, et al. ∙

research

∙ 10/29/2020

Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix

Estimating and storing the covariance (or correlation) matrix of high-di...

0 Zhenwei Dai, et al. ∙

research

∙ 07/20/2020

Early Stopping in Deep Networks: Double Descent and How to Eliminate it

Over-parameterized models, in particular deep networks, often exhibit a ...

29 Reinhard Heckel, et al. ∙

research

∙ 07/06/2020

Can Un-trained Neural Networks Compete with Trained Neural Networks at Image Reconstruction?

Convolutional Neural Networks (CNNs) are highly effective for image reco...

42 Mohammad Zalbagi Darestani, et al. ∙

research

∙ 05/07/2020

Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation

Un-trained convolutional neural networks have emerged as highly successf...

0 Reinhard Heckel, et al. ∙

research

∙ 01/23/2020

Reducing the Representation Error of GAN Image Priors Using the Deep Decoder

Generative models, such as GANs, learn an explicit low-dimensional repre...

5 Max Daniels, et al. ∙

research

∙ 01/16/2020

DNA-Based Storage: Models and Fundamental Limits

Due to its longevity and enormous information density, DNA is an attract...

0 Ilan Shomorony, et al. ∙

research

∙ 10/31/2019

Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators

Convolutional Neural Networks (CNNs) have emerged as highly successful t...

0 Reinhard Heckel, et al. ∙

research

∙ 10/20/2019

Leveraging inductive bias of neural networks for learning without explicit human annotations

Classification problems today are typically solved by first collecting e...

0 Fatih Furkan Yilmaz, et al. ∙

research

∙ 07/22/2019

Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients

Normalization layers are widely used in deep neural networks to stabiliz...

0 Zhenwei Dai, et al. ∙

research

∙ 07/06/2019

Regularizing linear inverse problems with convolutional neural networks

Deep convolutional neural networks trained on large datsets have emerged...

7 Reinhard Heckel, et al. ∙

research

∙ 02/27/2019

Capacity Results for the Noisy Shuffling Channel

Motivated by DNA-based storage, we study the noisy shuffling channel, wh...

0 Ilan Shomorony, et al. ∙

research

∙ 02/25/2019

Adaptive Estimation for Approximate k-Nearest-Neighbor Computations

Algorithms often carry out equally many computations for "easy" and "har...

0 Daniel LeJeune, et al. ∙

research

∙ 10/06/2018

Super-resolution radar imaging via convex optimization

A radar system emits probing signals and records the reflections. Estima...

0 Reinhard Heckel, et al. ∙

research

∙ 10/02/2018

Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks

Deep neural networks, in particular convolutional neural networks, have ...

0 Reinhard Heckel, et al. ∙

research

∙ 05/26/2018

Unsupervised Learning with Stein's Unbiased Risk Estimator

Learning from unlabeled and noisy data is one of the grand challenges of...

0 Christopher A. Metzler, et al. ∙

research

∙ 05/22/2018

Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior

Deep neural networks provide state-of-the-art performance for image deno...

0 Reinhard Heckel, et al. ∙

research

∙ 01/04/2018

Approximate Ranking from Pairwise Comparisons

A common problem in machine learning is to rank a set of n items based o...

0 Reinhard Heckel, et al. ∙

research

∙ 10/05/2017

DiffuserCam: Lensless Single-exposure 3D Imaging

We demonstrate a compact and easy-to-build computational camera for sing...

0 Nick Antipa, et al. ∙

research

∙ 05/31/2017

The Sample Complexity of Online One-Class Collaborative Filtering

We consider the online one-class collaborative filtering (CF) problem th...

0 Reinhard Heckel, et al. ∙

research

∙ 06/28/2016

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...

0 Reinhard Heckel, et al. ∙

research

∙ 04/27/2014

Subspace clustering of dimensionality-reduced data

Subspace clustering refers to the problem of clustering unlabeled high-d...

0 Reinhard Heckel, et al. ∙

research

∙ 03/13/2014

Neighborhood Selection for Thresholding-based Subspace Clustering

Subspace clustering refers to the problem of clustering high-dimensional...

0 Reinhard Heckel, et al. ∙

research

∙ 11/13/2013

Compressive Nonparametric Graphical Model Selection For Time Series

We propose a method for inferring the conditional indepen- dence graph (...

0 Alexander Jung, et al. ∙

research

∙ 07/18/2013

Robust Subspace Clustering via Thresholding

The problem of clustering noisy and incompletely observed high-dimension...

0 Reinhard Heckel, et al. ∙

research

∙ 05/15/2013

Noisy Subspace Clustering via Thresholding

We consider the problem of clustering noisy high-dimensional data points...

0 Reinhard Heckel, et al. ∙

research

∙ 03/15/2013

Subspace Clustering via Thresholding and Spectral Clustering

We consider the problem of clustering a set of high-dimensional data poi...

0 Reinhard Heckel, et al. ∙

Reinhard Heckel

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