Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning

• URL: http://arxiv.org/abs/2003.00231v2
• Date: Tue, 3 Mar 2020 04:52:37 GMT
• Title: Conjugate-gradient-based Adam for stochastic optimization and its application to deep learning
• Authors: Yu Kobayashi and Hideaki Iiduka
• Abstract summary: This paper proposes a conjugate-gradient-based Adam algorithm blending Adam with nonlinear conjugate gradient methods and shows its analysis. Numerical experiments on text classification and image classification show that the proposed algorithm can train deep neural network convergence in fewer epochs than the existing adaptive optimization algorithms can.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a conjugate-gradient-based Adam algorithm blending Adam with nonlinear conjugate gradient methods and shows its convergence analysis. Numerical experiments on text classification and image classification show that the proposed algorithm can train deep neural network models in fewer epochs than the existing adaptive stochastic optimization algorithms can.

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