Invention Grant
- Patent Title: System and method for learning the structure of deep convolutional neural networks
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Application No.: US15853403Application Date: 2017-12-22
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Publication No.: US11010658B2Publication Date: 2021-05-18
- Inventor: Guy Koren , Raanan Yonatan Yehezkel Rohekar , Shami Nisimov , Gal Novik
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Intel Corporation
- Current Assignee: Intel Corporation
- Current Assignee Address: US CA Santa Clara
- Agency: Schwegman Lundberg & Woessner, P.A.
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08 ; G06N7/00

Abstract:
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.
Public/Granted literature
- US20190042911A1 SYSTEM AND METHOD FOR LEARNING THE STRUCTURE OF DEEP CONVOLUTIONAL NEURAL NETWORKS Public/Granted day:2019-02-07
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