System and method for learning the structure of deep convolutional neural networks

A recursive method and apparatus produce a deep convolution neural network (CNN). The method iteratively processes an input directed acyclic graph (DAG) representing an initial CNN, a set of nodes, a set of exogenous nodes, and a resolution based on the CNN. An iteration for a node may include recur...

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Bibliographic Details
Main Authors Yehezkel Rohekar, Raanan Yonatan, Nisimov, Shami, Novik, Gal, Koren, Guy
Format Patent
LanguageEnglish
Published 18.05.2021
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:A recursive method and apparatus produce a deep convolution neural network (CNN). The method iteratively processes an input directed acyclic graph (DAG) representing an initial CNN, a set of nodes, a set of exogenous nodes, and a resolution based on the CNN. An iteration for a node may include recursively performing the iteration upon each node in a descendant node set to create a descendant DAG, and upon each node in ancestor node sets to create ancestor DAGs, the ancestor node sets being a remainder of nodes in the temporary DAG after removing nodes of the descendent node set. The descendant and ancestor DAGs are merged, and a latent layer is created that includes a latent node for each ancestor node set. Each latent node is set to be a parent of sets of parentless nodes in a combined descendant DAG and ancestors DAGs before returning.
Bibliography:Application Number: US201715853403