Summary of Adjusting Model Size in Continual Gaussian Processes: How Big Is Big Enough?, by Guiomar Pescador-barrios et al.
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
by Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers tackle the problem of determining the optimal size for machine learning models, particularly Gaussian processes in continual learning settings. The model size parameter affects performance and computational cost. They develop a method to automatically adjust model size while maintaining near-optimal performance. The approach is evaluated across diverse datasets with minimal hyperparameter tuning required. This work has implications for various applications where data becomes available incrementally. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on finding the right size for machine learning models, specifically in situations where new data keeps arriving. They investigate how to automatically adjust this size while keeping performance good. The solution is tested on many different datasets and works well with minimal setup needed. |
Keywords
» Artificial intelligence » Continual learning » Hyperparameter » Machine learning
