Summary of Fine-tuning — a Transfer Learning Approach, by Joseph Arul Raj et al.
Fine-tuning – a Transfer Learning approach
by Joseph Arul Raj, Linglong Qian, Zina Ibrahim
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper proposes a novel approach to handling missing data in Electronic Health Records (EHRs), which is crucial for medical analysis and decision-making. The existing deep imputation methods are end-to-end pipelines that combine both imputation and downstream analyses, making it difficult to assess the quality of imputation. To address this limitation, the authors develop a modular, deep learning-based imputation and classification pipeline that enables independent assessment of the imputer and classifier. This approach also allows for exploring simpler classification architectures using an optimized imputer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to handle missing data in Electronic Health Records (EHRs) which is important for medical analysis and decision-making. The existing methods are not good because they mix two things together – filling in the missing data and doing the actual analysis. This makes it hard to know if the missing data was filled in correctly or if the analysis worked well. The authors create a new way of doing this that separates these two steps, so you can test each part separately. |
Keywords
* Artificial intelligence * Classification * Deep learning
