Date of Award
Spring 2020
Degree Name
Bachelor of Arts
Department
Computer Science & Mathematics; College of Arts & Sciences
First Advisor
Dr. Carlos Ortiz
Abstract
Neural ordinary differential equations (ODEs) have recently emerged as a novel ap- proach to deep learning, leveraging the knowledge of two previously separate domains, neural networks and differential equations. In this paper, we first examine the back- ground and lay the foundation for traditional artificial neural networks. We then present neural ODEs from a rigorous mathematical perspective, and explore their advantages and trade-offs compared to traditional neural nets.
Recommended Citation
Nguyen, Long Huu and Malinsky, Andy, "Exploration and Implementation of Neural Ordinary Differential Equations" (2020). Capstone Showcase. 8.
https://scholarworks.arcadia.edu/showcase/2020/comp_sci_math/8
Included in
Artificial Intelligence and Robotics Commons, Ordinary Differential Equations and Applied Dynamics Commons
Exploration and Implementation of Neural Ordinary Differential Equations
Neural ordinary differential equations (ODEs) have recently emerged as a novel ap- proach to deep learning, leveraging the knowledge of two previously separate domains, neural networks and differential equations. In this paper, we first examine the back- ground and lay the foundation for traditional artificial neural networks. We then present neural ODEs from a rigorous mathematical perspective, and explore their advantages and trade-offs compared to traditional neural nets.