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Found 10 papers, 3 papers with code
SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models
However, diffusion models require careful tuning of inference hyperparameters on a validation set and are still sensitive to distribution shifts during testing.
Coil Sketching for computationally-efficient MR iterative reconstruction
Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non-Cartesian datasets.
ThinkSum: Probabilistic reasoning over sets using large language models
Our results suggest that because the probabilistic inference in ThinkSum is performed outside of calls to the LLM, ThinkSum is less sensitive to prompt design, yields more interpretable predictions, and can be flexibly combined with latent variable models to extract structured knowledge from LLMs.
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
However, they require several iterations of a large neural network to handle high-dimensional imaging tasks such as 3D MRI.
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks.
Greedy Learning for Large-Scale Neural MRI Reconstruction
Model-based deep learning approaches have recently shown state-of-the-art performance for accelerated MRI reconstruction.
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games.
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks.
Convex Regularization Behind Neural Reconstruction
Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems.
