报告题目：Variational deep neural networks for a class of inverse problems – models, algorithms and applications

主 讲 人：陈韵梅

单 位：University of Florida

时 间：11月8日、11日、15日9:30

腾 讯 ID：871-6791-8503

摘 要：Interpretability and generalizability of deep neural network learning methods are the main concerns and limitations when solving inverse problems in real-world applications. Despite of numerous empirical successes in recent years, deep-learning based methods are generally difficult to interpret and prune to overfitting, lack convergence guarantee, and can be extremely data demanding. In this short course, I would like to share our recent work to address those challenges. In the first talk, I will present our exact and inexact learned decent algorithms (LDAs), which induce efficient network architectures for solving a class of inverse problems. In the second talk, I will present our generalizable MRI reconstruction method with diverse dataset to tackle the task specific and extremely data demanding problems in deep learning based methods. In the last talk, I will discuss how to use multi-source/ multi-domain complementary information to improve the performance of deep neural networks.

简 介：陈韵梅，佛罗里达大学Distinguish Professor Emeritus、博士生导师。致力于数学、图像处理和机器学习等交叉学科的研究，研究领域涉及医学图像分析中数学模型的建立与数值优化方法的发展，并对其中潜在的数学理论进行了深入的研究。曾获中国国家自然科学奖三等奖和教育部科技进步奖一等奖，获国际发明专利9项，主持国家级项目30余项，在Inventiones Mathematicae, SIAM Journal on Imaging Science等杂志上发表学术论文200余篇。