Bypassing Stationary Points in Training Deep Learning Models.

Area of Science:

• Deep Learning
• Optimization Algorithms
• Computational Neuroscience

Background:

• Gradient-descent-based optimizers face training slowdowns due to stationary points in deep learning loss landscapes.
• Ubiquitous stationary points hinder the efficiency and convergence of neural network training.

Purpose of the Study:

• To introduce a novel method, the bypass pipeline, to actively rescue optimizers from slowdowns.
• To enhance the training efficiency of deep learning models by overcoming stationary point challenges.

Main Methods:

• The bypass algorithm extends the model space to circumvent stationary points.
• Function-preserving algebraic constraints are used to contract the model back to its original space.
• The method is implemented and verified for theoretically expected bypassing behaviors.

Main Results:

• Empirical benefits demonstrated in regression and classification benchmarks.
• The bypass algorithm shows computationally efficiency and compatibility with first-order optimizers.
• Verification of theoretically expected bypassing behaviors.

Conclusions:

• The bypass algorithm offers a practical solution to optimizer slowdowns in neural network training.
• This method revitalizes optimizers by dynamically adjusting the model space.
• Bypassing opens new research directions, including model-specific bypassing and neural architecture search (NAS).