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A scalable algorithm for the optimization of neural network architectures

Journal Article · · Parallel Computing
In this work, we propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to determine a neural network with minimal number of layers that is at least as performant as neural networks of the same structure identified by other hyperparameter search algorithms in terms of accuracy and computational cost. Numerical results performed on benchmark datasets show that, for these datasets, our method outperforms state-of-the-art hyperparameter optimization algorithms in terms of attainable predictive performance by the selected neural network architecture, and time-to-solution for the hyperparameter optimization to complete.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Organization:
USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)
Grant/Contract Number:
89233218CNA000001; AC05-00OR22725
OSTI ID:
1781383
Alternate ID(s):
OSTI ID: 1807280
OSTI ID: 1784469
Report Number(s):
LA-UR--21-20936
Journal Information:
Parallel Computing, Journal Name: Parallel Computing Vol. 104-105; ISSN 0167-8191
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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