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» Using model knowledge for learning inverse dynamics
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152
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NN
2002
Springer
208views Neural Networks» more  NN 2002»
15 years 3 months ago
A spiking neuron model: applications and learning
This paper presents a biologically-inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the mo...
Chris Christodoulou, Guido Bugmann, Trevor G. Clar...
COGSCI
2010
88views more  COGSCI 2010»
15 years 3 months ago
Domain-Creating Constraints
The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A lear...
Robert L. Goldstone, David Landy
AAAI
2010
15 years 4 months ago
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
ECAL
2003
Springer
15 years 8 months ago
Contextual Random Boolean Networks
Abstract. We propose the use of Deterministic Generalized Asynchronous Random Boolean Networks [1] as models of contextual deterministic discrete dynamical systems. We show that ch...
Carlos Gershenson, Jan Broekaert, Diederik Aerts
111
Voted
ICPR
2004
IEEE
16 years 4 months ago
Detection of Artificial Structures in Natural-Scene Images Using Dynamic Trees
We seek a framework that addresses localization, detection and recognition of man-made objects in natural-scene images in a unified manner. We propose to model artificial structur...
Michael C. Nechyba, Sinisa Todorovic