Search Results for author:
Found 9 papers, 4 papers with code
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.
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task.
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
We develop fast algorithms and robust software for convex optimization of two-layer neural networks with ReLU activation functions.
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.
Vector-output ReLU Neural Network Problems are Copositive Programs: Convex Analysis of Two Layer Networks and Polynomial-time Algorithms
We describe the convex semi-infinite dual of the two-layer vector-output ReLU neural network training problem.
Convex Regularization Behind Neural Reconstruction
Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems.
