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GECCO
2008
Springer
261views Optimization» more  GECCO 2008»
15 years 29 days ago
SSNNS -: a suite of tools to explore spiking neural networks
We are interested in engineering smart machines that enable backtracking of emergent behaviors. Our SSNNS simulator consists of hand-picked tools to explore spiking neural network...
Heike Sichtig, J. David Schaffer, Craig B. Laramee
GECCO
2010
Springer
173views Optimization» more  GECCO 2010»
15 years 3 months ago
The baldwin effect in developing neural networks
The Baldwin Effect is a very plausible, but unproven, biological theory concerning the power of learning to accelerate evolution. Simple computational models in the 1980’s gave...
Keith L. Downing
WAPCV
2004
Springer
15 years 5 months ago
TarzaNN: A General Purpose Neural Network Simulator for Visual Attention Modeling
A number of computational models of visual attention exist, but making comparisons is difficult due to the incompatible implementations and levels at which the simulations are con...
Albert L. Rothenstein, Andrei Zaharescu, John K. T...
NCA
2007
IEEE
14 years 11 months ago
Using evolution to improve neural network learning: pitfalls and solutions
: Autonomous neural network systems typically require fast learning and good generalization performance, and there is potentially a trade-off between the two. The use of evolutiona...
John A. Bullinaria
JMLR
2012
13 years 2 months ago
Random Search for Hyper-Parameter Optimization
Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are ...
James Bergstra, Yoshua Bengio