About Jinchao Xu Jinchao Xu Professor, Applied Mathematics and Computational Science applied mathematics deep learning PDEs Professor Xu is widely recognized as a leading figure in developing, designing and analyzing fast methods for finite element discretization and large-scale equation solutions. Events Presented Events Nov 20 - Nov 26, 2022 On training algorithms for neural networks Jinchao Xu, Professor, Applied Mathematics and Computational Science Nov 21, 12:00 - 13:00 B9 L2 R2322 H1 Deep learning deep neural networks In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms Sep 25 - Oct 1, 2022 Deep Learning and Scientific Computing Jinchao Xu, Professor, Applied Mathematics and Computational Science Sep 27, 12:00 - 13:00 B9 L2 R2322 Deep learning scientific computing In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.
On training algorithms for neural networks Jinchao Xu, Professor, Applied Mathematics and Computational Science Nov 21, 12:00 - 13:00 B9 L2 R2322 H1 Deep learning deep neural networks In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms
Deep Learning and Scientific Computing Jinchao Xu, Professor, Applied Mathematics and Computational Science Sep 27, 12:00 - 13:00 B9 L2 R2322 Deep learning scientific computing In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.
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