The Loss Surface Of Deep Linear Networks Viewed Through The Algebraic Geometry Lens

Author:

Mehta Dhagash1,Chen Tianran2,Tang Tingting3,Hauenstein Jonathan4

Affiliation:

1. the Autonomous and Intelligent Systems Department, East Hartfrot, Connecticut, United States, (e-mail: dhagashbmehta@gmail.com)

2. Mathematics and Computer Science, Auburn University at Montgomery, 15666 Montgomery, Alabama, United States, (e-mail: ti@nranchen.org)

3. Applied and computational Mathematics and Statistics, San Diego State University - Imperial Valley Campus, 52481 Calexico, California, United States, (e-mail: yiyit62@gmail.com)

4. Applied and computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, United States, (e-mail: hauenstein@nd.edu)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Function Space and Critical Points of Linear Convolutional Networks;SIAM Journal on Applied Algebra and Geometry;2024-05-21

2. Exact solutions of a deep linear network *;Journal of Statistical Mechanics: Theory and Experiment;2023-11-01

3. Geometry of Linear Convolutional Networks;SIAM Journal on Applied Algebra and Geometry;2022-08-02

4. On the landscape of one-hidden-layer sparse networks and beyond;Artificial Intelligence;2022-08

5. A weight initialization based on the linear product structure for neural networks;Applied Mathematics and Computation;2022-02

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