Supervised machine learning with tensor network kernel machines
In this talk Kim Batselier will introduce tensor network kernel machines. These models are able to learn nonlinear patterns from data for both regression and classification tasks and are described by an exponential amount of model parameters. Live-demos will show that such models can be learned efficiently and at the same time achieve state-of-the art performance on validation data.
Praktische info:
Leertraject
georganiseerd door:
Lesgever/spreker
Delf University of Technology, the Netherlands
Back to the Roots Seminar Series
The ERC research project "Back to the roots of data-driven dynamical system identification", led by Prof. Dr. Bart De Moor (KU Leuven, ESAT-STADIUS), focuses on system identification, where mathematical models are derived from observed data generated by systems such as medical monitoring, electricity consumption and industrial processes. Utilizing optimization algorithms, one seeks to identify the best model in a chosen model class. This methodology finds widespread application across thousands of use cases within the AI community. However, there is no guarantee that optimization algorithms will find the best model. Present-day optimization practices are heuristic in nature, yielding results that may not be reproducible and consequently difficult to interpret.
The main objective of the Back to the Roots project is to develop a theoretical framework that combines model classes and optimization algorithms, enabling the calculation of the optimal model within the specified model class with 100% certainty.
