HyperNeuralTuringMachine
Implementation of HyperNTM
What is HyperNEAT?
HyperNEAT is based on a theory of representation that hypothesizes that a good representation for an artificial neural network should be able to describe its pattern of connectivity compactly. This kind of description is called an encoding. The encoding in HyperNEAT, called compositional pattern producing networks, is designed to represent patterns with regularities such as symmetry, repetition, and repetition with variation. (Click here for an example of CPPN-generated patterns.) Thus HyperNEAT is able to evolve neural networks with these properties. The main implication of this capability is that HyperNEAT can efficiently evolve very large neural networks that look more like neural connectivity patterns in the brain (which are repetitious with many regularities, in addition to some irregularities) and that are generally much larger than what prior approaches to neural learning could produce.