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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...
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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...
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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...
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07/24/2023
Learning Provably Robust Estimators for Inverse Problems via Jittering
Deep neural networks provide excellent performance for inverse problems ...
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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 ...
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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...
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03/20/2023
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data
Recently, self-supervised neural networks have shown excellent image den...
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11/10/2022
Information-Theoretic Foundations of DNA Data Storage
Due to its longevity and enormous information density, DNA is an attract...
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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 ...
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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...
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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...
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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...
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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...
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02/04/2022
Image-to-Image MLP-mixer for Image Reconstruction
Neural networks are highly effective tools for image reconstruction prob...
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12/09/2021
Provable Continual Learning via Sketched Jacobian Approximations
An important problem in machine learning is the ability to learn tasks i...
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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...
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09/24/2021
Untrained Graph Neural Networks for Denoising
A fundamental problem in signal processing is to denoise a signal. While...
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08/05/2021
Interpolation can hurt robust generalization even when there is no noise
Numerous recent works show that overparameterization implicitly reduces ...
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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...
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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...
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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 ...
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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...
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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...
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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...
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01/16/2020
DNA-Based Storage: Models and Fundamental Limits
Due to its longevity and enormous information density, DNA is an attract...
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10/31/2019
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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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...
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07/22/2019
Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients
Normalization layers are widely used in deep neural networks to stabiliz...
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07/06/2019
Regularizing linear inverse problems with convolutional neural networks
Deep convolutional neural networks trained on large datsets have emerged...
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02/27/2019
Capacity Results for the Noisy Shuffling Channel
Motivated by DNA-based storage, we study the noisy shuffling channel, wh...
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02/25/2019
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations
Algorithms often carry out equally many computations for "easy" and "har...
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10/06/2018
Super-resolution radar imaging via convex optimization
A radar system emits probing signals and records the reflections. Estima...
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10/02/2018
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
Deep neural networks, in particular convolutional neural networks, have ...
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05/26/2018
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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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...
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01/04/2018
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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10/05/2017
DiffuserCam: Lensless Single-exposure 3D Imaging
We demonstrate a compact and easy-to-build computational camera for sing...
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05/31/2017
The Sample Complexity of Online One-Class Collaborative Filtering
We consider the online one-class collaborative filtering (CF) problem th...
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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...
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04/27/2014
Subspace clustering of dimensionality-reduced data
Subspace clustering refers to the problem of clustering unlabeled high-d...
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03/13/2014
Neighborhood Selection for Thresholding-based Subspace Clustering
Subspace clustering refers to the problem of clustering high-dimensional...
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11/13/2013
Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen- dence graph (...
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07/18/2013
Robust Subspace Clustering via Thresholding
The problem of clustering noisy and incompletely observed high-dimension...
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05/15/2013
Noisy Subspace Clustering via Thresholding
We consider the problem of clustering noisy high-dimensional data points...
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03/15/2013