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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem...
10/22/2020 ∙ by Bahareh Tolooshams, et al. ∙
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Dense and Sparse Coding: Theory and Architectures
The sparse representation model has been successfully utilized in a numb...
06/16/2020 ∙ by Abiy Tasissa, et al. ∙
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Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Supervised deep learning has gained significant attention for speech enh...
01/30/2020 ∙ by Bahareh Tolooshams, et al. ∙
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RandNet: deep learning with compressed measurements of images
Principal component analysis, dictionary learning, and auto-encoders are...
08/25/2019 ∙ by Thomas Chang, et al. ∙
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Convolutional Dictionary Learning in Hierarchical Networks
Filter banks are a popular tool for the analysis of piecewise smooth sig...
07/23/2019 ∙ by Javier Zazo, et al. ∙
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Deep Exponential-Family Auto-Encoders
We consider the problem of learning recurring convolutional patterns fro...
07/07/2019 ∙ by Bahareh Tolooshams, et al. ∙
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Deep Residual Auto-Encoders for Expectation Maximization-based Dictionary Learning
Convolutional dictionary learning (CDL) has become a popular method for ...
04/18/2019 ∙ by Bahareh Tolooshams, et al. ∙
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Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
Given a convolutional dictionary underlying a set of observed signals, c...
07/12/2018 ∙ by Bahareh Tolooshams, et al. ∙
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