Summary of Learning Rate Optimization For Deep Neural Networks Using Lipschitz Bandits, by Padma Priyanka et al.
Learning Rate Optimization for Deep Neural Networks Using Lipschitz Bandits
by Padma Priyanka, Sheetal Kalyani, Avhishek Chatterjee
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A Lipschitz bandit-driven approach is proposed for tuning the learning rate of neural networks, which outperforms HyperOpt and BLiE in terms of finding a better learning rate using fewer evaluations and epochs. The method leverages bandit-based optimization to efficiently train neural networks with reduced training time and computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach helps neural networks learn faster and more accurately by optimizing the learning rate through a clever algorithm that combines Lipschitz continuity and bandit-based optimization. It’s like having a superpower for your neural network, allowing it to adapt quickly to new information without wasting time or resources! |
Keywords
» Artificial intelligence » Neural network » Optimization
