Model Orthogonalization - Class Distance Hardening in Neural Networks for Better Security
Offered By: IEEE via YouTube
Course Description
Overview
Explore a 19-minute IEEE conference talk on enhancing neural network security through model orthogonalization and class distance hardening. Delve into the research conducted by a team from Purdue University, including Guanhong Tao, Yingqi Liu, Guangyu Shen, Qiuling Xu, Shengwei An, Zhuo Zhang, and Xiangyu Zhang. Learn about innovative techniques to improve the robustness and reliability of neural networks in the face of security challenges, and gain insights into the potential applications of these methods in various fields of artificial intelligence and machine learning.
Syllabus
Model Orthogonalization: Class Distance Hardening in Neural Networks for Better Security
Taught by
IEEE Symposium on Security and Privacy
Tags
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent