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» The Learning Power of Evolution
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144
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NECO
2008
170views more  NECO 2008»
15 years 3 months ago
Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wis...
Nicolas Le Roux, Yoshua Bengio
122
Voted
IJHCI
2010
144views more  IJHCI 2010»
15 years 2 months ago
Naturalistic Decision Making for Power System Operators
Motivation – Investigations of large-scale outages in the North American interconnected electric system often attribute the causes to three T’s: Trees, Training and Tools. To ...
Frank L. Greitzer, Robin Podmore, Marck Robinson, ...
124
Voted
AI
2007
Springer
15 years 3 months ago
An economist's perspective on multi-agent learning
We comment on the Shoham, Powers, and Grenager survey of multi-agent learning and game theory, emphasizing that some of their categories are important for economics and others are...
Drew Fudenberg, David K. Levine
102
Voted
COLCOM
2007
IEEE
15 years 10 months ago
Evolving a text-based conferencing system: An experience report
—In this paper we describe the evolution of eConference, a text-based conferencing system that has turned into a collaborative platform. We draw the lessons learned from the evol...
Fabio Calefato, Filippo Lanubile, Mario Scalas
141
Voted
EDUTAINMENT
2007
Springer
15 years 10 months ago
Using Metaphors in Game-Based Education
In spite of its growing popularity, due to a huge technical evolution in the last years and to the fact that new generations are more literate in games than in books, game-based te...
Pedro Pablo Gómez-Martín, Marco Anto...