Summary of No Learning Rates Needed: Introducing Salsa — Stable Armijo Line Search Adaptation, by Philip Kenneweg et al.
No learning rates needed: Introducing SALSA – Stable Armijo Line Search Adaptation
by Philip Kenneweg, Tristan Kenneweg, Fabian Fumagalli, Barbara Hammer
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed enhancements to line search methods significantly improve their performance across various datasets and architectures, making the choice of learning rate schedule unnecessary. The authors identify issues with current state-of-the-art line search methods, propose solutions, and rigorously evaluate their effectiveness on large-scale datasets and complex data domains. Specifically, they enhance the Armijo line search method by accelerating its computation and incorporating momentum into the Armijo criterion, making it suitable for stochastic mini-batching. This optimization approach outperforms both previous Armijo implementations and tuned learning rate schedules for Adam and SGD optimizers. The evaluation covers a range of architectures (Transformers, CNNs, MLPs) and data domains (NLP, image data). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Line search methods help improve the performance of machine learning models. In this study, researchers found that current line search methods have some problems. They proposed ways to fix these issues and tested their ideas on really big datasets with complex data. The authors also made an improvement to a specific type of line search method called Armijo. This new version is faster and works well with small batches of training data. The study shows that this approach performs better than other methods for Adam and SGD optimization algorithms. It was tested on different types of models (Transformers, CNNs, MLPs) and data types (natural language processing, image recognition). |
Keywords
* Artificial intelligence * Machine learning * Natural language processing * Nlp * Optimization
