A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks

@article{Dogo2018ACA,
  title={A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks},
  author={Eustace M. Dogo and Oluwatobi Joshua Afolabi and Nnamdi Ikechi Nwulu and Bhekisipho Twala and Clinton Ohis Aigbavboa},
  journal={2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)},
  year={2018},
  pages={92-99}
}
  • E. Dogo, O. J. Afolabi, C. Aigbavboa
  • Published 1 December 2018
  • Computer Science
  • 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)
In this paper, we perform a comparative evaluation of seven most commonly used first-order stochastic gradient-based optimization techniques in a simple Convolutional Neural Network (ConvNet) architectural setup. [] Key Method The investigated techniques are the Stochastic Gradient Descent (SGD), with vanilla (vSGD), with momentum (SGDm), with momentum and nesterov (SGDm+n)), Root Mean Square Propagation (RMSProp), Adaptive Moment Estimation (Adam), Adaptive Gradient (AdaGrad), Adaptive Delta (AdaDelta…

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