Campus Event Calendar
Event Entry
What and Who
Representing and Learning Structure in Dynamical Systems
Professor Thomas Dean
Brown University
Providence, USA
DFKI-Kolloquium
AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, RG1, SWS
AG Audience
English
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Date, Time and Location
Wednesday, 21 August 96
16:00
60 Minutes
43.1 - DFKI
1.01
Saarbrücken
Abstract
A dynamical system is a mathematical model of a physical process.
A state is a complete description of a dynamical system at an instant in time. Structure refers to properties of a dynamical system that make it possible to compactly represent its states and the associated temporal relations involving those states.
This talk investigates the issues involved in representing and learning dynamical systems using graphical models that consist of a set of state variables that aggregate states and functions that relate these state variables. This representation allows us to make explicit the structure in a process and suggests learning
algorithms to exploit this structure computationally. We link previous work in learning dynamical systems to current theory and practice in learning Bayesian networks. We establish the connection between techniques in computer science concerned with inferring finite automata and those in physics, speech recognition and time series analysis concerned with recovering the dynamics of nonlinear dynamical systems.
A state is a complete description of a dynamical system at an instant in time. Structure refers to properties of a dynamical system that make it possible to compactly represent its states and the associated temporal relations involving those states.
This talk investigates the issues involved in representing and learning dynamical systems using graphical models that consist of a set of state variables that aggregate states and functions that relate these state variables. This representation allows us to make explicit the structure in a process and suggests learning
algorithms to exploit this structure computationally. We link previous work in learning dynamical systems to current theory and practice in learning Bayesian networks. We establish the connection between techniques in computer science concerned with inferring finite automata and those in physics, speech recognition and time series analysis concerned with recovering the dynamics of nonlinear dynamical systems.
Contact
Susanne Biundo
5256
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Tags, Category, Keywords and additional notes
Professor Dean besucht das DFKI vom 19. - 23. August 1996.
TERMINÄNDERUNG: 16:00 statt 15:00 Uhr