Summary of Dynamic Estimation Of Learning Rates Using a Non-linear Autoregressive Model, by Ramin Okhrati
Dynamic Estimation of Learning Rates Using a Non-Linear Autoregressive Model
by Ramin Okhrati
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 new class of adaptive non-linear autoregressive (Nlar) models is introduced, which dynamically estimates both learning rates and momentum as iterations increase. The growth of gradients is controlled using a scaling function for stable convergence. Three distinct estimators for learning rates are proposed, with theoretical proof of their convergence. These estimators underpin the development of effective Nlar optimizers. Performance is evaluated through extensive experiments on various datasets and a reinforcement learning environment. Results show robust convergence despite parameter variations, including large initial learning rates, and strong adaptability with rapid convergence during initial epochs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new type of model helps machines learn better by adjusting how they update their knowledge as they go along. This “momentum” helps the model stay stable while making progress. The model has three different ways to figure out how fast it should learn, and these are proven to work well. The results show that this new model is good at adapting to changes in its training data and can even handle large initial mistakes. |
Keywords
» Artificial intelligence » Autoregressive » Reinforcement learning
