Morteza Mardani | DeepAI

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∙ 05/07/2023

A Variational Perspective on Solving Inverse Problems with Diffusion Models

Diffusion models have emerged as a key pillar of foundation models in vi...

0 Morteza Mardani, et al. ∙

∙ 03/22/2023

Multiscale Attention via Wavelet Neural Operators for Vision Transformers

Transformers have achieved widespread success in computer vision. At the...

0 Anahita Nekoozadeh, et al. ∙

∙ 08/08/2022

FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators

Extreme weather amplified by climate change is causing increasingly deva...

8 Thorsten Kurth, et al. ∙

∙ 07/18/2022

GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction

Unrolled neural networks have recently achieved state-of-the-art acceler...

9 Batu Ozturkler, et al. ∙

∙ 05/17/2022

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers

Vision transformers using self-attention or its proposed alternatives ha...

14 Arda Sahiner, et al. ∙

∙ 02/22/2022

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

FourCastNet, short for Fourier Forecasting Neural Network, is a global d...

64 Jaideep Pathak, et al. ∙

∙ 07/12/2021

Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions

Generative Adversarial Networks (GANs) are commonly used for modeling co...

13 Arda Sahiner, et al. ∙

∙ 03/02/2021

Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization

Batch Normalization (BN) is a commonly used technique to accelerate and ...

16 Tolga Ergen, et al. ∙

∙ 12/09/2020

Convex Regularization Behind Neural Reconstruction

Neural networks have shown tremendous potential for reconstructing high-...

4 Arda Sahiner, et al. ∙

∙ 09/30/2020

Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images

Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a wid...

16 Edgar A. Rios Piedra, et al. ∙

∙ 10/15/2019

Wasserstein GANs for MR Imaging: from Paired to Unpaired Training

Lack of ground-truth MR images (labels) impedes the common supervised tr...

44 Ke Lei, et al. ∙

∙ 06/10/2019

Degrees of Freedom Analysis of Unrolled Neural Networks

Unrolled neural networks emerged recently as an effective model for lear...

5 Morteza Mardani, et al. ∙

∙ 03/19/2019

Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning

Compressed sensing in MRI enables high subsampling factors while maintai...

22 Joseph Y. Cheng, et al. ∙

∙ 01/31/2019

VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis

Recovering high-quality images from limited sensory data is a challengin...

12 Vineet Edupuganti, et al. ∙

∙ 06/01/2018

Neural Proximal Gradient Descent for Compressive Imaging

Recovering high-resolution images from limited sensory data typically le...

0 Morteza Mardani, et al. ∙

∙ 11/27/2017

Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery

Recovering images from undersampled linear measurements typically leads ...

0 Morteza Mardani, et al. ∙

∙ 05/31/2017

Deep Generative Adversarial Networks for Compressed Sensing Automates MRI

Magnetic resonance image (MRI) reconstruction is a severely ill-posed li...

0 Morteza Mardani, et al. ∙

∙ 09/27/2016

Online Categorical Subspace Learning for Sketching Big Data with Misses

With the scale of data growing every day, reducing the dimensionality (a...

0 Yanning Shen, et al. ∙

∙ 09/14/2016

Tracking Tensor Subspaces with Informative Random Sampling for Real-Time MR Imaging

Magnetic resonance imaging (MRI) nowadays serves as an important modalit...

0 Morteza Mardani, et al. ∙

∙ 04/17/2014

Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

Extracting latent low-dimensional structure from high-dimensional data i...

0 Morteza Mardani, et al. ∙

∙ 04/30/2012

Recovery of Low-Rank Plus Compressed Sparse Matrices with Application to Unveiling Traffic Anomalies

Given the superposition of a low-rank matrix plus the product of a known...

0 Morteza Mardani, et al. ∙

∙ 03/07/2012

In-network Sparsity-regularized Rank Minimization: Algorithms and Applications

Given a limited number of entries from the superposition of a low-rank m...

0 Morteza Mardani, et al. ∙

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