A Physics-Inspired Optimizer: Velocity Regularized Adam
📰 ArXiv cs.AI
Learn how to implement Velocity-Regularized Adam, a physics-inspired optimizer that stabilizes training dynamics for deep neural networks
Action Steps
- Implement VRAdam in your deep learning framework using the provided mathematical formulation
- Compare the performance of VRAdam with other optimizers like Adam on your specific task
- Analyze the effect of velocity regularization on the training dynamics of your model
- Apply VRAdam to train deep neural networks with improved stability and convergence
- Test the robustness of VRAdam on various system dynamics and loss landscapes
Who Needs to Know This
Machine learning engineers and researchers can benefit from this optimizer to improve the stability and convergence of their models during training
Key Insight
💡 Velocity regularization can improve the stability and convergence of deep neural network training
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🚀 Introducing VRAdam, a physics-inspired optimizer that stabilizes deep learning training dynamics! 🤖
Key Takeaways
Learn how to implement Velocity-Regularized Adam, a physics-inspired optimizer that stabilizes training dynamics for deep neural networks
Full Article
Title: A Physics-Inspired Optimizer: Velocity Regularized Adam
Abstract:
arXiv:2505.13196v3 Announce Type: replace-cross Abstract: We introduce Velocity-Regularized Adam (VRAdam), a physics-inspired optimizer for training deep neural networks that draws on ideas from quartic terms for kinetic energy with its stabilizing effects on various system dynamics. Previous algorithms, including the ubiquitous Adam, operate at the so-called adaptive edge of stability regime during training, leading to rapid oscillations and slowed convergence of loss. However, VRAdam adds a hi
Abstract:
arXiv:2505.13196v3 Announce Type: replace-cross Abstract: We introduce Velocity-Regularized Adam (VRAdam), a physics-inspired optimizer for training deep neural networks that draws on ideas from quartic terms for kinetic energy with its stabilizing effects on various system dynamics. Previous algorithms, including the ubiquitous Adam, operate at the so-called adaptive edge of stability regime during training, leading to rapid oscillations and slowed convergence of loss. However, VRAdam adds a hi
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