Explore a 29-minute lecture from the Fourth Symposium on Machine Learning and Dynamical Systems, delivered by Isao Ishikawa from Ehime University. Delve into the intricacies of finite-dimensional approximations of push-forwards on locally analytic functionals, gaining insights into this advanced mathematical concept and its potential applications in machine learning and dynamical systems.
On Finite-Dimensional Approximations of Push-Forwards on Locally Analytic Functionals
Overview
Syllabus
On finite-dimensional approximations of push-forwards on locally analytic functionals
Taught by
Fields Institute
Related Courses
-
Low-Dimensional Approximations of the Conditional Law of Volterra Processes - A Non-Positive Curvature Approach
-
Finite Dimensional Approximations of Groups
-
Efficient Approximation of Infinite-Dimensional Dynamical Systems in Finance
-
Statistical Learning of Transfer Operators and the Infinitesimal Generator
-
The Operator is the Model - Dynamical Systems and Machine Learning
-
Identifying Nonlinear Dynamics with High Confidence from Sparse Data
