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09/05/2023
Approximating High-Dimensional Minimal Surfaces with Physics-Informed Neural Networks
In this paper, we compute numerical approximations of the minimal surfac...
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06/05/2023
Learned Alternating Minimization Algorithm for Dual-domain Sparse-View CT Reconstruction
We propose a novel Learned Alternating Minimization Algorithm (LAMA) for...
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05/31/2023
Parameterized Wasserstein Hamiltonian Flow
In this work, we propose a numerical method to compute the Wasserstein H...
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01/31/2023
Neural Control of Parametric Solutions for High-dimensional Evolution PDEs
We develop a novel computational framework to approximate solution opera...
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04/08/2022
A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis
Generating multi-contrasts/modal MRI of the same anatomy enriches diagno...
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10/15/2021
Low-rank Matrix Recovery With Unknown Correspondence
We study a matrix recovery problem with unknown correspondence: given th...
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10/02/2021
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset
Purpose: This work aims at developing a generalizable MRI reconstruction...
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09/27/2021
Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximation
We develop a versatile deep neural network architecture, called Lyapunov...
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09/20/2021
An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities
Goal: This work aims at developing a novel calibration-free fast paralle...
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06/03/2021
Influence Estimation and Maximization via Neural Mean-Field Dynamics
We propose a novel learning framework using neural mean-field (NMF) dyna...
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04/27/2021
Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstruction
We propose a provably convergent method, called Efficient Learned Descen...
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11/30/2020
A Hypergradient Approach to Robust Regression without Correspondence
We consider a regression problem, where the correspondence between input...
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08/04/2020
Deep Parallel MRI Reconstruction Network Without Coil Sensitivities
We propose a novel deep neural network architecture by mapping the robus...
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07/22/2020
Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction
We propose a general learning based framework for solving nonsmooth and ...
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06/16/2020
Network Diffusions via Neural Mean-Field Dynamics
We propose a novel learning framework based on neural mean-field dynamic...
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03/15/2020
A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
Optimization algorithms for solving nonconvex inverse problem have attra...
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02/26/2020
Numerical Solution of Inverse Problems by Weak Adversarial Networks
We consider a weak adversarial network approach to numerically solve a c...
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02/22/2020
Learning Cost Functions for Optimal Transport
Learning the cost function for optimal transport from observed transport...
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07/18/2019
Weak Adversarial Networks for High-dimensional Partial Differential Equations
Solving general high-dimensional partial differential equations (PDE) is...
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02/10/2018
Learning to Recommend via Inverse Optimal Matching
We consider recommendation in the context of optimal matching, i.e., we ...
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11/08/2017
Learning Deep Mean Field Games for Modeling Large Population Behavior
We consider the problem of representing collective behavior of large pop...
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11/08/2017
Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations
We consider the problem of representing a large population's behavior po...
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05/23/2017
Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous seque...
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05/16/2014