Practical Solutions and Value of Weight Decay and Regularization in Deep Learning
Significance of Weight Decay and Regularization
Weight decay and ℓ2 regularization are essential in machine learning to limit network capacity and eliminate irrelevant weight components, aligning with Occam’s razor principles. They are central in optimizing generalization bounds.
Challenges in Modern Deep Learning
Despite being widely used in advanced networks like GPT-3 and CLIP, the full impact of weight decay remains unclear due to new architectures like transformers. Recent studies have raised doubts about the direct correlation between norm-based measures and generalization.
Recent Progress and Insights
Recent research has shed light on the unique effects of weight decay and ℓ2 regularization, especially in optimizing dynamics. It influences learning rates in scale-invariant networks, regularizes input Jacobians, and damps effects in certain optimizers.
New Perspectives on Weight Decay
Researchers challenge the traditional view of weight decay as a mere regularizer, emphasizing its role in modifying optimization dynamics. It enhances stability in low-precision training and accelerates optimization processes, particularly in bfloat16 mixed-precision training.
Key Findings
Weight decay enables stable bfloat16 training, reducing memory usage and facilitating larger model training. It prevents late-training spikes that can affect model performance and resolves precision-related issues in float16 training.
Future Directions
The study highlights the importance of optimization speed and training stability in modern deep learning, offering insights into successful weight decay implementation across various architectures. Future approaches focus on model training and hyperparameter tuning.
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