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GECCO
2010
Springer
168views Optimization» more  GECCO 2010»
14 years 13 days ago
Investigating whether hyperNEAT produces modular neural networks
HyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a ionally efficient high-level abstraction of development....
Jeff Clune, Benjamin E. Beckmann, Philip K. McKinl...
IJCNN
2006
IEEE
14 years 1 months ago
Reconstruction of Gene Regulatory Networks from Temporal Microarray Data Using Pattern Recognition Techniques
- Gene regulatory networks allow us to study and understand genes’ roles in biological processes. Among others, regulatory networks help to identify pathway initiator genes and t...
Azhar Salim, Faramarz Valafar
NECO
2007
129views more  NECO 2007»
13 years 7 months ago
Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance
It is well-known that, in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its ac...
Shinichi Nakajima, Sumio Watanabe
BC
1998
84views more  BC 1998»
13 years 7 months ago
Stimulus-induced bifurcations in discrete-time neural oscillators
Abstract. Based on theoretical issues and neurobiological evidence, considerable interest has recently focused on dynamic computational elements in neural systems. Such elements re...
Ali A. Minai, Tirunelveli Anand
DCC
2010
IEEE
13 years 6 months ago
Neural Markovian Predictive Compression: An Algorithm for Online Lossless Data Compression
This work proposes a novel practical and general-purpose lossless compression algorithm named Neural Markovian Predictive Compression (NMPC), based on a novel combination of Bayesi...
Erez Shermer, Mireille Avigal, Dana Shapira