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» Load estimation and control using learned dynamics models
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ICML
2004
IEEE
14 years 8 months ago
Learning and discovery of predictive state representations in dynamical systems with reset
Predictive state representations (PSRs) are a recently proposed way of modeling controlled dynamical systems. PSR-based models use predictions of observable outcomes of tests that...
Michael R. James, Satinder P. Singh
ESANN
1998
13 years 9 months ago
Lazy learning for control design
This paper presents two local methods for the control of discrete-time unknown nonlinear dynamical systems, when only a limited amount of input-output data is available. The modeli...
Gianluca Bontempi, Mauro Birattari, Hugues Bersini
ECAI
2006
Springer
13 years 11 months ago
Learning Behaviors Models for Robot Execution Control
Robust execution of robotic tasks is a difficult problem. In many situations, these tasks involve complex behaviors combining different functionalities (e.g. perception, localizat...
Guillaume Infantes, Félix Ingrand, Malik Gh...
ICML
2010
IEEE
13 years 8 months ago
Learning Deep Boltzmann Machines using Adaptive MCMC
When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with man...
Ruslan Salakhutdinov
IWCMC
2010
ACM
13 years 9 months ago
On the use of random neural networks for traffic matrix estimation in large-scale IP networks
Despite a large body of literature and methods devoted to the Traffic Matrix (TM) estimation problem, the inference of traffic flows volume from aggregated data still represents a ...
Pedro Casas, Sandrine Vaton