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» Load estimation and control using learned dynamics models
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ICML
2004
IEEE
16 years 3 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
15 years 4 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
15 years 6 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
15 years 4 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
15 years 5 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