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NCI
2004
185views Neural Networks» more  NCI 2004»
15 years 1 months ago
Hierarchical reinforcement learning with subpolicies specializing for learned subgoals
This paper describes a method for hierarchical reinforcement learning in which high-level policies automatically discover subgoals, and low-level policies learn to specialize for ...
Bram Bakker, Jürgen Schmidhuber
ICANN
2010
Springer
15 years 28 days ago
Visualising Clusters in Self-Organising Maps with Minimum Spanning Trees
Abstract. The Self-Organising Map (SOM) is a well-known neuralnetwork model that has successfully been used as a data analysis tool in many different domains. The SOM provides a to...
Rudolf Mayer, Andreas Rauber
ICANN
2010
Springer
15 years 28 days ago
A Bilinear Model for Consistent Topographic Representations
Visual recognition faces the difficult problem of recognizing objects despite the multitude of their appearances. Ample neuroscientific evidence shows that the cortex uses a topogr...
Urs Bergmann, Christoph von der Malsburg
EAAI
2007
90views more  EAAI 2007»
14 years 11 months ago
AI techniques in modelling, assignment, problem solving and optimization
This paper recapitulates the results of a long research on a family of artificial intelligence (AI) methods—relying on, e.g., artificial neural networks and search techniques...
Zsolt János Viharos, Zsolt Kemény
TNN
2008
93views more  TNN 2008»
14 years 11 months ago
Towards the Optimal Design of Numerical Experiments
This paper addresses the problem of the optimal design of numerical experiments for the construction of nonlinear surrogate models. We describe a new method, called learner disagre...
S. Gazut, J.-M. Martinez, Gérard Dreyfus, Y...