455 citations · 1.5k across the 28 of their papers we have counts for
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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…
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…
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…
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…