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GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
14 years 2 months ago
Generating large-scale neural networks through discovering geometric regularities
Connectivity patterns in biological brains exhibit many repeating motifs. This repetition mirrors inherent geometric regularities in the physical world. For example, stimuli that ...
Jason Gauci, Kenneth O. Stanley
GECCO
1999
Springer
130views Optimization» more  GECCO 1999»
14 years 5 days ago
Heterochrony and Adaptation in Developing Neural Networks
This paper discusses the simulation results of a model of biological development for neural networks based on a regulatory genome. The model’s results are analyzed using the fra...
Angelo Cangelosi
NN
2002
Springer
136views Neural Networks» more  NN 2002»
13 years 7 months ago
Bayesian model search for mixture models based on optimizing variational bounds
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
Naonori Ueda, Zoubin Ghahramani
NN
2006
Springer
13 years 7 months ago
Use of a neuro-variational inversion for retrieving oceanic and atmospheric constituents from satellite ocean colour sensor: App
This paper presents a new development of the NeuroVaria method. NeuroVaria computes relevant atmospheric and oceanic parameters by minimizing the difference between the observed s...
Julien Brajard, Cédric Jamet, Cyril Moulin,...
GECCO
2007
Springer
158views Optimization» more  GECCO 2007»
14 years 2 months ago
A novel generative encoding for exploiting neural network sensor and output geometry
A significant problem for evolving artificial neural networks is that the physical arrangement of sensors and effectors is invisible to the evolutionary algorithm. For example,...
David B. D'Ambrosio, Kenneth O. Stanley