System and method for building artificial neural network architectures

Abstract

There is disclosed a novel system and method for building artificial neural networks for a given task. In an embodiment, the method utilizes one or more network models that define the probabilities of nodes and/or interconnects, and/or the probabilities of groups of nodes and/or interconnects, from sets of possible nodes and interconnects existing in a given artificial neural network. These network models can be constructed based on the properties of one or more artificial neural networks, or constructed based on desired architecture properties. These network models are then used to build combined network models using a model combiner module. The combined network models and random numbers generated by a random number generator module are then used to build one or more new artificial neural network architectures. New artificial neural networks are then built based on the newly built artificial neural network architectures and are trained for a given task. These trained artificial neural networks can then be used to generate network models for building subsequent artificial neural network architectures. This iterative building process can be repeated in order to learn how to build new artificial neural network architectures, and this learning may be stored to build future artificial neural network architectures based on past neural network architectures.

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