Summary of Cel: a Continual Learning Model For Disease Outbreak Prediction by Leveraging Domain Adaptation Via Elastic Weight Consolidation, By Saba Aslam et al.
CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation
by Saba Aslam, Abdur Rasool, Hongyan Wu, Xiaoli Li
First submitted to arxiv on: 17 Jan 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 This paper introduces a novel Continual Learning (CEL) model that leverages domain adaptation via Elastic Weight Consolidation (EWC) to mitigate the catastrophic forgetting phenomenon in deep neural networks. The CEL model is designed for dynamic fields like disease outbreak prediction, where models need to learn over time without forgetting previous knowledge. By constructing a regularization term using the Fisher Information Matrix (FIM), CEL aims to penalize changes to important parameters and retain previous knowledge. The paper evaluates CEL’s performance on three distinct diseases – Influenza, Mpox, and Measles – and demonstrates its superiority in adapting to incremental data with high R-squared values during evaluation and reevaluation. CEL’s robustness and reliability are underscored by its minimal 65% forgetting rate and 18% higher memory stability compared to existing benchmark studies. This study highlights the versatility of CEL in disease outbreak prediction, offering a valuable model for proactive disease control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make machines learn over time without forgetting what they already know. This is important because some problems, like predicting diseases, need machines to adapt to changing data. The new method uses something called Elastic Weight Consolidation (EWC) to help the machine remember what it learned before. The researchers tested this method on three different diseases and showed that it works better than other methods. They also found that their method is more reliable and doesn’t forget as much as others do. |
Keywords
* Artificial intelligence * Continual learning * Domain adaptation * Regularization
