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Title: Optimizing neural networks

Patent ·
OSTI ID:1925038

A system and method design and optimize neural networks. The system and method include a data store that stores a plurality of gene vectors that represent diverse and distinct neural networks and an evaluation queue stored with the plurality of gene vectors. Secondary nodes construct, train, and evaluate the neural network and automatically render a plurality of fitness values asynchronously. A primary node executes a gene amplification on a select plurality of gene vectors, a crossing-over of the amplified gene vectors, and a mutation of the crossing-over gene vectors automatically and asynchronously, which are then transmitted to the evaluation queue. The process continuously repeats itself by processing the gene vectors inserted into the evaluation queue until a fitness level is reached, a network's accuracy level plateaus, a processing time period expires, or when some stopping condition or performance metric is met or exceeded.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
Assignee:
UT-Battelle, LLC (Oak Ridge, TN)
Patent Number(s):
11,429,865
Application Number:
16/265,252
OSTI ID:
1925038
Resource Relation:
Patent File Date: 02/01/2019
Country of Publication:
United States
Language:
English

References (4)

Execution of a genetic algorithm with variable evolutionary weights of topological parameters for neural network generation and training patent-application March 2019
Evolving Deep Networks Using HPC conference January 2017
Vertex reconstruction of neutrino interactions using deep learning conference May 2017
GPU-based asynchronous particle swarm optimization conference July 2011