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06/05/2023
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Latent Gaussian models have a rich history in statistics and machine lea...
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09/28/2022
Learning Filter-Based Compressed Blind-Deconvolution
The problem of sparse multichannel blind deconvolution (S-MBD) arises fr...
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12/09/2021
A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks
Deep learning-based speech enhancement has shown unprecedented performan...
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05/31/2021
PUDLE: Implicit Acceleration of Dictionary Learning by Backpropagation
The dictionary learning problem, representing data as a combination of f...
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03/28/2021
Gaussian Process Convolutional Dictionary Learning
Convolutional dictionary learning (CDL), the problem of estimating shift...
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02/13/2021
On the convergence of group-sparse autoencoders
Recent approaches in the theoretical analysis of model-based deep learni...
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10/22/2020
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem...
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06/16/2020
Dense and Sparse Coding: Theory and Architectures
The sparse representation model has been successfully utilized in a numb...
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01/30/2020
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Supervised deep learning has gained significant attention for speech enh...
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08/25/2019
RandNet: deep learning with compressed measurements of images
Principal component analysis, dictionary learning, and auto-encoders are...
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07/23/2019
Convolutional Dictionary Learning in Hierarchical Networks
Filter banks are a popular tool for the analysis of piecewise smooth sig...
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07/07/2019
Deep Exponential-Family Auto-Encoders
We consider the problem of learning recurring convolutional patterns fro...
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04/18/2019
Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary Learning
Convolutional dictionary learning (CDL) has become a popular method for ...
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07/12/2018