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...
share
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...
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...
research
∙
07/24/2023
Learning Provably Robust Estimators for Inverse Problems via Jittering
Deep neural networks provide excellent performance for inverse problems ...
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 ...
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...
research
∙
03/20/2023
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data
Recently, self-supervised neural networks have shown excellent image den...
research
∙
11/10/2022
Information-Theoretic Foundations of DNA Data Storage
Due to its longevity and enormous information density, DNA is an attract...
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 ...
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...
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...
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...
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...
research
∙
02/04/2022
Image-to-Image MLP-mixer for Image Reconstruction
Neural networks are highly effective tools for image reconstruction prob...
research
∙
12/09/2021
Provable Continual Learning via Sketched Jacobian Approximations
An important problem in machine learning is the ability to learn tasks i...
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...
research
∙
09/24/2021
Untrained Graph Neural Networks for Denoising
A fundamental problem in signal processing is to denoise a signal. While...
research
∙
08/05/2021
Interpolation can hurt robust generalization even when there is no noise
Numerous recent works show that overparameterization implicitly reduces ...
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...
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...
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 ...
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...
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...
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...
research
∙
01/16/2020
DNA-Based Storage: Models and Fundamental Limits
Due to its longevity and enormous information density, DNA is an attract...
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...
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...
research
∙
07/22/2019
Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients
Normalization layers are widely used in deep neural networks to stabiliz...
research
∙
07/06/2019
Regularizing linear inverse problems with convolutional neural networks
Deep convolutional neural networks trained on large datsets have emerged...
research
∙
02/27/2019
Capacity Results for the Noisy Shuffling Channel
Motivated by DNA-based storage, we study the noisy shuffling channel, wh...
research
∙
02/25/2019
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations
Algorithms often carry out equally many computations for "easy" and "har...
research
∙
10/06/2018
Super-resolution radar imaging via convex optimization
A radar system emits probing signals and records the reflections. Estima...
research
∙
10/02/2018
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
Deep neural networks, in particular convolutional neural networks, have ...
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...
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...
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...
research
∙
10/05/2017
DiffuserCam: Lensless Single-exposure 3D Imaging
We demonstrate a compact and easy-to-build computational camera for sing...
research
∙
05/31/2017
The Sample Complexity of Online One-Class Collaborative Filtering
We consider the online one-class collaborative filtering (CF) problem th...
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...
research
∙
04/27/2014
Subspace clustering of dimensionality-reduced data
Subspace clustering refers to the problem of clustering unlabeled high-d...
research
∙
03/13/2014
Neighborhood Selection for Thresholding-based Subspace Clustering
Subspace clustering refers to the problem of clustering high-dimensional...
research
∙
11/13/2013
Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen- dence graph (...
research
∙
07/18/2013
Robust Subspace Clustering via Thresholding
The problem of clustering noisy and incompletely observed high-dimension...
research
∙
05/15/2013
Noisy Subspace Clustering via Thresholding
We consider the problem of clustering noisy high-dimensional data points...
research
∙
03/15/2013
