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09/12/2023
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Inverse problems arise in a multitude of applications, where the goal is...
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07/25/2023
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Quasi-Newton methods still face significant challenges in training large...
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07/02/2023
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Deep learning models are prone to forgetting information learned in the ...
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03/25/2023
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
Diffusion models have established new state of the art in a multitude of...
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03/15/2022
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction
In accelerated MRI reconstruction, the anatomy of a patient is recovered...
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06/28/2021
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Deep neural networks have emerged as very successful tools for image res...
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06/16/2020
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Transfer learning has emerged as a powerful technique for improving the ...
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02/26/2020
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow
Imaging 3D nano-structures at very high resolution is crucial in a varie...
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06/12/2019