Structure and Performance of Fully Connected Neural Networks: Emerging Complex Network Properties

Understand

Understanding the behavior of Artificial Neural Networks is one of the main topics in the field recently, as black-box approaches have become usual since the widespread of deep learning.

• Such high-dimensional models may manifest instabilities and weird properties that resemble complex systems.
• Therefore, we propose Complex Network (CN) techniques to analyze the structure and performance of fully connected neural networks.
• For that, we build a dataset with 4 thousand models and their respective CN properties.