Morteza Mardani

Orcid: 0000-0002-3788-137X

According to our database1, Morteza Mardani authored at least 59 papers between 2008 and 2024.

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Bibliography

2024
Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling.
CoRR, 2024

Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales.
CoRR, 2024

Repulsive Score Distillation for Diverse Sampling of Diffusion Models.
CoRR, 2024

DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations.
CoRR, 2024

AGG: Amortized Generative 3D Gaussians for Single Image to 3D.
CoRR, 2024

Compositional Text-to-Image Generation with Dense Blob Representations.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Scaling Convex Neural Networks with Burer-Monteiro Factorization.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Variational Perspective on Solving Inverse Problems with Diffusion Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Generative Residual Diffusion Modeling for Km-scale Atmospheric Downscaling.
CoRR, 2023

Multiscale Attention via Wavelet Neural Operators for Vision Transformers.
CoRR, 2023

FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators.
Proceedings of the Platform for Advanced Scientific Computing Conference, 2023

SMRD: SURE-Based Robust MRI Reconstruction with Diffusion Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation.
Proceedings of the International Conference on Machine Learning, 2023

Pseudoinverse-Guided Diffusion Models for Inverse Problems.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction.
CoRR, 2022

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.
CoRR, 2022

Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers.
Proceedings of the International Conference on Machine Learning, 2022

Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Wasserstein GANs for MR Imaging: From Paired to Unpaired Training.
IEEE Trans. Medical Imaging, 2021

Uncertainty Quantification in Deep MRI Reconstruction.
IEEE Trans. Medical Imaging, 2021

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.
CoRR, 2021

Convex Regularization behind Neural Reconstruction.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Compressed Sensing: From Research to Clinical Practice With Deep Neural Networks: Shortening Scan Times for Magnetic Resonance Imaging.
IEEE Signal Process. Mag., 2020

Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images.
CoRR, 2020

Neural FFTs for Universal Texture Image Synthesis.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020

2019
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.
IEEE Trans. Medical Imaging, 2019

Degrees of Freedom Analysis of Unrolled Neural Networks.
CoRR, 2019

Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning.
CoRR, 2019

VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis.
CoRR, 2019

2018
Neural Proximal Gradient Descent for Compressive Imaging.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Online Categorical Subspace Learning for Sketching Big Data with Misses.
IEEE Trans. Signal Process., 2017

Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery.
CoRR, 2017

Deep Generative Adversarial Networks for Compressed Sensing Automates MRI.
CoRR, 2017

Recurrent generative adversarial neural networks for compressive imaging.
Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

2016
Estimating Traffic and Anomaly Maps via Network Tomography.
IEEE/ACM Trans. Netw., 2016

Tracking Tensor Subspaces with Informative Random Sampling for Real-Time MR Imaging.
CoRR, 2016

2015
Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors.
IEEE Trans. Signal Process., 2015

Online sketching for big data subspace learning.
Proceedings of the 23rd European Signal Processing Conference, 2015

Online sketching of big categorical data with absent features.
Proceedings of the 49th Annual Conference on Information Sciences and Systems, 2015

2014
Imputation of streaming low-rank tensor data.
Proceedings of the IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, 2014

Classification of streaming big data with misses.
Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2013
Decentralized Sparsity-Regularized Rank Minimization: Algorithms and Applications.
IEEE Trans. Signal Process., 2013

Recovery of Low-Rank Plus Compressed Sparse Matrices With Application to Unveiling Traffic Anomalies.
IEEE Trans. Inf. Theory, 2013

Dynamic Anomalography: Tracking Network Anomalies Via Sparsity and Low Rank.
IEEE J. Sel. Top. Signal Process., 2013

Rank minimization for subspace tracking from incomplete data.
Proceedings of the IEEE International Conference on Acoustics, 2013

Robust network traffic estimation via sparsity and low rank.
Proceedings of the IEEE International Conference on Acoustics, 2013

Robust tomography via network traffic maps leveraging sparsity and low rank.
Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013

2012
Cross-Layer Design of Wireless Multihop Random Access Networks.
IEEE Trans. Signal Process., 2012

In-network Sparsity-regularized Rank Minimization: Algorithms and Applications
CoRR, 2012

Exact recovery of low-rank plus compressed sparse matrices.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Distributed nuclear norm minimization for matrix completion.
Proceedings of the 13th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2012

2011
Link-Adaptive and QoS-Provisioning Cooperative ARQ - Applications to Relay-Assisted Land Mobile Satellite Communications.
IEEE Trans. Veh. Technol., 2011

Unveiling anomalies in large-scale networks via sparsity and low rank.
Proceedings of the Conference Record of the Forty Fifth Asilomar Conference on Signals, 2011

2008
Joint Adaptive Modulation Coding and Cooperative ARQ over Relay Channels-Applications to Land Mobile Satellite Communications
CoRR, 2008

Cross-Layer Link Adaptation Design for Relay Channels with Cooperative ARQ Protocol
CoRR, 2008

Joint adaptive modulation-coding and cooperative ARQ for wireless relay networks.
Proceedings of the 2008 5th International Symposium on Wireless Communication Systems, 2008


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