Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks

Area of Science:

• Deep Learning
• Stochastic Optimization

Background:

• Nonconvex stochastic optimization is crucial for deep learning.
• Adaptive-learning-rate algorithms like Adam and AMSGrad are widely used but require appropriate learning rates.

Purpose of the Study:

• To derive theoretically grounded learning rates for adaptive optimization algorithms in nonconvex settings.
• To demonstrate faster convergence compared to existing methods.
• To evaluate the performance of constant versus diminishing learning rates.

Main Methods:

• Theoretical analysis of nonconvex stochastic optimization problems.
• Development of adaptive learning rate strategies.
• Numerical experiments on text and image classification tasks.

Main Results:

• New learning rates are proposed for algorithms like Adam and AMSGrad, enabling faster convergence.
• Constant learning rates demonstrated superior performance over diminishing learning rates in practical deep learning applications.
• Experimental validation on text and image classification confirmed theoretical findings.

Conclusions:

• The proposed learning rates offer theoretical and practical advantages for deep learning optimization.
• Constant learning rates are recommended for adaptive optimization algorithms in deep learning tasks, challenging conventional approaches.