research
∙
09/14/2023
Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts
Sifting through vast textual data and summarizing key information impose...
share
research
∙
05/02/2023
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
We systematically investigate lightweight strategies to adapt large lang...
research
∙
05/17/2022
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Vision transformers using self-attention or its proposed alternatives ha...
research
∙
04/21/2022
Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
Unrolled neural networks have enabled state-of-the-art reconstruction pe...
research
∙
08/24/2021
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
Image reconstruction is an inverse problem that solves for a computation...
research
∙
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...
research
∙
05/25/2021
A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction
Deep learning affords enormous opportunities to augment the armamentariu...
research
∙
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 ...
research
∙
12/24/2020
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...
research
∙
12/09/2020
Convex Regularization Behind Neural Reconstruction
Neural networks have shown tremendous potential for reconstructing high-...
research
∙
06/10/2019
Degrees of Freedom Analysis of Unrolled Neural Networks
Unrolled neural networks emerged recently as an effective model for lear...
research
∙
01/31/2019
VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis
Recovering high-quality images from limited sensory data is a challengin...
research
∙
12/09/2018
Deep Learning with Attention to Predict Gestational Age of the Fetal Brain
Fetal brain imaging is a cornerstone of prenatal screening and early dia...
research
∙
06/01/2018
Neural Proximal Gradient Descent for Compressive Imaging
Recovering high-resolution images from limited sensory data typically le...
research
∙
12/12/2017
200x Low-dose PET Reconstruction using Deep Learning
Positron emission tomography (PET) is widely used in various clinical ap...
research
∙
11/27/2017
