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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...
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03/22/2023
Multiscale Attention via Wavelet Neural Operators for Vision Transformers
Transformers have achieved widespread success in computer vision. At the...
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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...
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07/18/2022
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Unrolled neural networks have recently achieved state-of-the-art acceler...
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05/17/2022
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Vision transformers using self-attention or its proposed alternatives ha...
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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...
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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...
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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 ...
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12/09/2020
Convex Regularization Behind Neural Reconstruction
Neural networks have shown tremendous potential for reconstructing high-...
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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...
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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...
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06/10/2019
Degrees of Freedom Analysis of Unrolled Neural Networks
Unrolled neural networks emerged recently as an effective model for lear...
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03/19/2019
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Compressed sensing in MRI enables high subsampling factors while maintai...
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01/31/2019
VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis
Recovering high-quality images from limited sensory data is a challengin...
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06/01/2018
Neural Proximal Gradient Descent for Compressive Imaging
Recovering high-resolution images from limited sensory data typically le...
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11/27/2017
Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery
Recovering images from undersampled linear measurements typically leads ...
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05/31/2017
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Magnetic resonance image (MRI) reconstruction is a severely ill-posed li...
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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...
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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...
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04/17/2014
Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors
Extracting latent low-dimensional structure from high-dimensional data i...
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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...
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03/07/2012