455 citations · 1.5k across the 28 of their papers we have counts for
29 papers
AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI
Cagan Alkan, Morteza Mardani, Congyu Liao +3
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however…
A Variational Perspective on Solving Inverse Problems with Diffusion Models
Morteza Mardani, Jiaming Song, Jan Kautz +1
Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks…
Multiscale Attention via Wavelet Neural Operators for Vision Transformers
Anahita Nekoozadeh, Mohammad Reza Ahmadzadeh, Zahra Mardani
Transformers have achieved widespread success in computer vision. At their heart, there is a Self-Attention (SA) mechanism, an inductive bias that associates each token in the inpu…
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Thorsten Kurth, Shashank Subramanian, Peter Harrington +6
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits…
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen +6
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between phys…
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Arda Sahiner, Tolga Ergen, Batu Ozturkler +3
Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks. However, the underpinning inductive bias of a…
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Jaideep Pathak, Shashank Subramanian, Peter Harrington +10
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.2…
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
John Guibas, Morteza Mardani, Zongyi Li +3
Vision transformers have delivered tremendous success in representation learning. This is primarily due to effective token mixing through self attention. However, this scales quadr…
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
Arda Sahiner, Tolga Ergen, Batu Ozturkler +4
Generative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural netw…
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Tolga Ergen, Arda Sahiner, Batu Ozturkler +3
Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks. Despite its empirical success, a full theoretical understanding…
Convex Regularization Behind Neural Reconstruction
Arda Sahiner, Morteza Mardani, Batu Ozturkler +2
Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders…
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images
Edgar A. Rios Piedra, Morteza Mardani, Frank Ong +3
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice. DCE and similar datasets of dynamic medi…
Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
Ke Lei, Morteza Mardani, John M. Pauly +1
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this paper leverages unpaired advers…
Degrees of Freedom Analysis of Unrolled Neural Networks
Morteza Mardani, Qingyun Sun, Vardan Papyan +3
Unrolled neural networks emerged recently as an effective model for learning inverse maps appearing in image restoration tasks. However, their generalization risk (i.e., test mean-…
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Joseph Y. Cheng, Feiyu Chen, Christopher Sandino +3
Compressed sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolut…
Uncertainty Quantification in Deep MRI Reconstruction
Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala +1
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non-tran…
Neural Proximal Gradient Descent for Compressive Imaging
Morteza Mardani, Qingyun Sun, Shreyas Vasawanala +4
Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prio…
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Morteza Mardani, Hatef Monajemi, Vardan Papyan +3
Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors i…
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Morteza Mardani, Enhao Gong, Joseph Y. Cheng +8
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it a…
Online Categorical Subspace Learning for Sketching Big Data with Misses
Yanning Shen, Morteza Mardani, Georgios B. Giannakis
With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to…
Tracking Tensor Subspaces with Informative Random Sampling for Real-Time MR Imaging
Morteza Mardani, Georgios B. Giannakis, Kamil Ugurbil
Magnetic resonance imaging (MRI) nowadays serves as an important modality for diagnostic and therapeutic guidance in clinics. However, the {\it slow acquisition} process, the dynam…
Estimating Traffic and Anomaly Maps via Network Tomography
Morteza Mardani, Georgios B. Giannakis
Mapping origin-destination (OD) network traffic is pivotal for network management and proactive security tasks. However, lack of sufficient flow-level measurements as well as poten…
Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors
Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis
Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with `Big Data' analytics. However, increasi…
Dynamic Anomalography: Tracking Network Anomalies via Sparsity and Low Rank
Morteza Mardani, Gonzalo Mateos, Georgios B. Giannakis
In the backbone of large-scale networks, origin-to-destination (OD) traffic flows experience abrupt unusual changes known as traffic volume anomalies, which can result in congestio…