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09/14/2023
Uncertainty quantification for learned ISTA
Model-based deep learning solutions to inverse problems have attracted i...
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06/22/2023
Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models
Recent works have shown that line search methods can speed up Stochastic...
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05/09/2023
Robust Implicit Regularization via Weight Normalization
Overparameterized models may have many interpolating solutions; implicit...
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12/30/2022
Uncertainty quantification for sparse Fourier recovery
One of the most prominent methods for uncertainty quantification in high...
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12/21/2021
More is Less: Inducing Sparsity via Overparameterization
In deep learning it is common to overparameterize the neural networks, t...
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12/08/2021
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks
Motivated by the learned iterative soft thresholding algorithm (LISTA), ...
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10/13/2021
Spark Deficient Gabor Frames for Inverse Problems
In this paper, we apply star-Digital Gabor Transform in analysis Compres...
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10/13/2021
ADMM-DAD net: a deep unfolding network for analysis compressed sensing
In this paper, we propose a new deep unfolding neural network based on t...
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08/06/2021
Path classification by stochastic linear recurrent neural networks
We investigate the functioning of a classifying biological neural networ...
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08/04/2021
Convergence of gradient descent for learning linear neural networks
We study the convergence properties of gradient descent for training dee...
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06/11/2021
New challenges in covariance estimation: multiple structures and coarse quantization
In this self-contained chapter, we revisit a fundamental problem of mult...
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04/29/2021
Star DGT: a Robust Gabor Transform for Speech Denoising
In this paper, we address the speech denoising problem, where white Gaus...
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04/02/2021
Covariance estimation under one-bit quantization
We consider the classical problem of estimating the covariance matrix of...
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03/20/2021
Spark Deficient Gabor Frame Provides a Novel Analysis Operator for Compressed Sensing
The analysis sparsity model is a very effective approach in modern Compr...
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11/27/2020
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank
We provide an explicit analysis of the dynamics of vanilla gradient desc...
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10/29/2020
Generalization bounds for deep thresholding networks
We consider compressive sensing in the scenario where the sparsity basis...
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10/13/2020
Unfolding recurrence by Green's functions for optimized reservoir computing
Cortical networks are strongly recurrent, and neurons have intrinsic tem...
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06/15/2020
Weighted Optimization: better generalization by smoother interpolation
We provide a rigorous analysis of how implicit bias towards smooth inter...
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05/14/2020
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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10/12/2019
Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
We study the convergence of gradient flows related to learning deep line...
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07/16/2019
