A Novel Framework for Neural Architecture Search in the Hill Climbing Domain

• URL: http://arxiv.org/abs/2102.12985v1
• Date: Mon, 22 Feb 2021 04:34:29 GMT
• Title: A Novel Framework for Neural Architecture Search in the Hill Climbing Domain
• Authors: Mudit Verma, Pradyumna Sinha, Karan Goyal, Apoorva Verma and Seba Susan
• Abstract summary: We propose a new framework for neural architecture search based on a hill-climbing procedure. We achieve a 4.96% error rate on the CIFAR-10 dataset in 19.4 hours of a single GPU training.
• Score: 2.729898906885749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks have now long been used for solving complex problems of image domain, yet designing the same needs manual expertise. Furthermore, techniques for automatically generating a suitable deep learning architecture for a given dataset have frequently made use of reinforcement learning and evolutionary methods which take extensive computational resources and time. We propose a new framework for neural architecture search based on a hill-climbing procedure using morphism operators that makes use of a novel gradient update scheme. The update is based on the aging of neural network layers and results in the reduction in the overall training time. This technique can search in a broader search space which subsequently yields competitive results. We achieve a 4.96% error rate on the CIFAR-10 dataset in 19.4 hours of a single GPU training.

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