Summary of Learning the Regularization Strength For Deep Fine-tuning Via a Data-emphasized Variational Objective, by Ethan Harvey et al.
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective
by Ethan Harvey, Mikhail Petrov, Michael C. Hughes
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper proposes an alternative approach to traditional grid search methods for selecting regularization hyperparameters in transfer learning models. The authors suggest directly learning these hyperparameters using model selection techniques based on the evidence lower bound (ELBo) objective from variational methods. This approach overcomes the limitations of grid search, including computational expense, reduced data availability, and requiring practitioners to specify candidate values. Instead, the proposed technique learns regularization hyperparameters on the full training set, allowing for more efficient model development and improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper offers a new way to find the right balance between the complexity of a deep learning model and how well it fits the available data. This is important because finding the right balance is hard, especially when you have lots of data. The authors suggest using a special type of math problem called an “evidence lower bound” to help figure out which balance works best. They also propose a way to make this approach more efficient and effective. By learning how to choose these balances in a better way, the authors hope to make it easier for people to build good models without having to do lots of extra work. |
Keywords
» Artificial intelligence » Deep learning » Grid search » Regularization » Transfer learning
