Search Results for author:
Found 18 papers, 7 papers with code
Ultrasound Lung Aeration Map via Physics-Aware Neural Operators
Lung ultrasound is a growing modality in clinics for diagnosing and monitoring acute and chronic lung diseases due to its low cost and accessibility.
A Unified Model for Compressed Sensing MRI Across Undersampling Patterns
Our unified model offers a versatile solution for MRI, adapting seamlessly to various measurement undersampling and imaging resolutions, making it highly effective for flexible and reliable clinical imaging.
Diffusion State-Guided Projected Gradient for Inverse Problems
DiffStateGrad, as a module, can be added to a wide range of diffusion-based inverse solvers to improve the preservation of the diffusion process on the prior manifold and filter out artifact-inducing components.
Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems
In this manuscript, we use FNOs to model the evolution of random quantum spin systems, so chosen due to their representative quantum dynamics and minimal symmetry.
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Latent Gaussian models have a rich history in statistics and machine learning, with applications ranging from factor analysis to compressed sensing to time series analysis.
Unrolled Compressed Blind-Deconvolution
To reduce its computational and implementation cost, we propose a compression method that enables blind recovery from much fewer measurements with respect to the full received signal in time.
A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks
Deep learning-based speech enhancement has shown unprecedented performance in recent years.
Stable and Interpretable Unrolled Dictionary Learning
The success of dictionary learning relies on access to a "good" initial estimate of the dictionary and the ability of the sparse coding step to provide an unbiased estimate of the code.
Gaussian Process Convolutional Dictionary Learning
Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in the absence of a prior/structure on the templates.
On the convergence of group-sparse autoencoders
Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from generative models whose hidden layers are sparse.
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution.
Towards improving discriminative reconstruction via simultaneous dense and sparse coding
We propose a novel dense and sparse coding model that integrates both representation capability and discriminative features.
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Supervised deep learning has gained significant attention for speech enhancement recently.
Ranked #2 on
Speech Enhancement
on CHiME-3
RandNet: deep learning with compressed measurements of images
We introduce a class of neural networks, termed RandNet, for learning representations using compressed random measurements of data of interest, such as images.
Convolutional Dictionary Learning in Hierarchical Networks
Motivated by the empirically observed properties of scale and detail coefficients of images in the wavelet domain, we propose a hierarchical deep generative model of piecewise smooth signals that is a recursion across scales: the low pass scale coefficients at one layer are obtained by filtering the scale coefficients at the next layer, and adding a high pass detail innovation obtained by filtering a sparse vector.
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions
We introduce a class of auto-encoder neural networks tailored to data from the natural exponential family (e. g., count data).
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning
Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias).
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
We demonstrate the ability of CRsAE to recover the underlying dictionary and characterize its sensitivity as a function of SNR.
