Summary of Quantifying Emergence in Neural Networks: Insights From Pruning and Training Dynamics, by Faisal Alshinaifi et al.
Quantifying Emergence in Neural Networks: Insights from Pruning and Training Dynamics
by Faisal AlShinaifi, Zeyad Almoaigel, Johnny Jingze Li, Abdulla Kuleib, Gabriel A. Silva
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper introduces a quantitative framework to measure emergence during the training process of neural networks. The authors examine how emergence impacts network performance, particularly with pruning and training dynamics. They hypothesize that the degree of emergence can predict the development of emergent behaviors in the network. Through experiments on benchmark datasets, they find that higher emergence correlates with improved trainability and performance. Additionally, they explore the relationship between network complexity and the loss landscape, suggesting that higher emergence indicates a greater concentration of local minima and a more rugged loss landscape. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how neural networks work better. It shows how complex behaviors can develop from simple parts inside the network, which is important for making neural networks more efficient and effective. The researchers used special frameworks to measure this process and found that when it happens, the network performs better. They also looked at what happens when they remove some parts of the network, called pruning, and found that it makes training faster but might make the final result not as good. |
Keywords
» Artificial intelligence » Pruning
