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GECCO
2010
Springer
168views Optimization» more  GECCO 2010»
15 years 9 months 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...
GECCO
2010
Springer
173views Optimization» more  GECCO 2010»
15 years 9 months ago
Evolving the placement and density of neurons in the hyperneat substrate
The Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach demonstrated that the pattern of weights across the connectivity of an artificial neural network ...
Sebastian Risi, Joel Lehman, Kenneth O. Stanley
ICSE
2010
IEEE-ACM
15 years 9 months ago
Adaptive bug isolation
Statistical debugging uses lightweight instrumentation and statistical models to identify program behaviors that are strongly predictive of failure. However, most software is most...
Piramanayagam Arumuga Nainar, Ben Liblit
ITS
2010
Springer
157views Multimedia» more  ITS 2010»
15 years 9 months ago
A Computational Model of Accelerated Future Learning through Feature Recognition
Accelerated future learning, in which learning proceeds more effectively and more rapidly because of prior learning, is considered to be one of the most interesting measures of ro...
Nan Li, William W. Cohen, Kenneth R. Koedinger
IWMM
2010
Springer
118views Hardware» more  IWMM 2010»
15 years 9 months ago
Speculative parallelization using state separation and multiple value prediction
With the availability of chip multiprocessor (CMP) and simultaneous multithreading (SMT) machines, extracting thread level parallelism from a sequential program has become crucial...
Chen Tian, Min Feng, Rajiv Gupta
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